Maven7 Blog

What Twitter suggests for the New York primary

2016-04-19T09:05:00.000+02:00

With Donald Trump's overwhelming dominance, there are no surprises on the Republican front. However, the remaining primary elections hold the answer to perhaps the most important question for the Democrats: Clinton or Sanders? According to Twitter, Sanders.

In order to gain a better understanding of the possible outcomes of the upcoming, and highly critical, New York primary, our team at Diktio Labs took a different approach and left the polls behind. Instead, we monitored and analyzed activity on Twitter.

Between April 11 and April 15, 2016, we analyzed 151,965 tweets by 36,703 accounts containing the hashtag #NYPrimary, with the purpose of identifying key influencers, topic clusters, and of course the popularity of candidates in the Empire State.

#Trump2016 vs. #FeelTheBern

Despite the fact that Hillary Clinton is more widely considered to emerge victorious from the NY primary, and eventually become the candidate of the Democrats, Bernie Sanders and his supporters have a much stronger online presence. In fact, Sanders is the only candidate, thanks to his active supporters, who has a slight chance of diluting Trump's online dominance.

Sanders supporters tweet vigorously, hence they represent over 50% of the Top 30 most active accounts. (We have manually removed bots.)

When it comes to influence however, results look slightly different. The Top 30 most retweeted accounts are in favor of Trump, but only with a mere 47%.

We categorized the most influential accounts by different dimensions of: well-connected, most mentioned, accounts with most outward activity and community brokers.

Well-connected accounts are those that receive the most attention in the NY primary conversations on Twitter from other accounts that have many followers. Trump’s supporters dominate this list. Noticeable are media outlets, such as NY1, CNN, POLITICO, and ABC News, and journalists, like Jeanine Ramirez covering stories around the primaries, who are often cited and followed by users interested in the primaries.

For most mentioned, there is only one account that makes it to the list for Cruz, four supporting Clinton, while the rest of the top 30 are Trump and Sanders supporters.

Most outward activity: accounts producing the most tweets in support for a candidate seem to be overwhelmingly pro-Sanders. However, most of them do not make it into the well-connected list because they are rather poorly embedded into Twitter conversations around the election.

Community brokers are accounts that bridge conversations about their preferred candidates between groups of users who would otherwise not communicate. They mostly engage in debated topics by following conversations in other camps and replying to those.

In our analysis, we also examined the top 30 hashtags mentioned in order to gain a better understanding of the topics surrounding the New York primary.

Many hashtags are used to reference events or places. They are usually used in conjunction with candidate-related hashtags to mobilize followers to join rallies, follow ongoing or future events, and encourage voting.

Trump’s campaign-related hashtags receive the most attention, followed by Sanders. Clinton is referenced mostly through the hashtag #ImWithHer, rather than nominally. The supporters of Cruz mention their preferred candidate much less than the other supporters.

The hashtag #NeverTrump is considered the only negative tag in the Top 30.

In order to identify the top 30 most mentioned (interesting/debated/adored/hated) candidates, we took a look at candidate mentions, on a user level. Example: how many users talked about candidates, during the analyzed time frame. We have visualized our results on a network map. The size of nodes and labels indicates the number of users mentioning the name of the candidate, while the connecting lines (edges) reflect how many users talked about both candidates. The color of the node indicates party affiliations: red is for Republicans, blue is for Democrats.

The most mentioned candidate was Donald Trump with more than 2,000 more users talking about him than about Bernie Sanders. Kasich was the least mentioned candidate. Clinton and Cruz are mentioned almost by the same number of users, which is less than half the number of users mentioning Trump.

In terms of users talking about two candidates, the biggest overlap is between Clinton and Sanders, followed by Trump and Cruz. The difference is more than 1,000 users. The remaining combinations were not so frequent apart from that of Clinton and Trump, mentioned by two percent of the users using the hashtag #NYPrimary.

This interactive, clickable and zoomable network map, (filtered to indicate top connections only) depicts what hashtags were mentioned together within the same tweet in our dataset. The thicker the edge, the more often the two hashtags were mentioned together. Node and label sizes indicate how often the given hashtag was mentioned together with other hashtags [network science metric: weighted degree].

Over 19% of the tweets associated #NYPrimary with #Trump2016, and almost 16% with #FeelTheBern. #ImWithHer: 8.6% of tweets. The different color clusters depict various topics around the NY Primary. Dark orange hashtags represent upcoming primaries: #PAPrimary, #MDPrimary, #DEPrimary, #CTPrimary, #RIPrimary, dark blue ones are the most associated hashtags with New York, while the light orange hashtags with the Democratic party.

Will the domination on Twitter manifest in the elections?

So far, our analyses clearly show that Trump and Sanders dominate Twitter conversations about the NY primary, each in their own community of supporters and opponents. The NY primary are expected to have a higher than average turnout in the state of New York, which might bring surprises, at least for the Democrats, where, according to polls, the two candidates are in a tight race for the win.

Please bear in mind that an online analysis does not intend to replace the offline polls or in itself cannot predict the results of the actual elections. Our experience in political analysis suggests that smaller parties and “anti-establishment” candidates tend to be more active on social media. The NY primaries are further complicated by the fact that the registration was over six months ago, thus however successful a candidate is on social media now, it might be too late to turn the popularity gained into actual votes. Given the race is not over between the candidates, there is a lot to learn in the forthcoming months.

Are you interested in the methods behind our analysis, and how you could benefit from online community mapping? Reach out to us via email at info@diktiolabs.com

What the numbers say - 2015 in the life of Maven7

2015-12-18T16:45:00.000+01:00

2015 is slowly coming to an end, and while we are already fully focused on the future, let us reminisce about the year behind us. Maven7 has had an exciting year, packed with great challenges, great projects and even greater moments.

Strategically, 2015 was of key importance as the company received a set of Series A funding, making the total investment in Maven7 $2.3 million to date.

Team-wise, we have grown to 32 with the joining of 8 new mavens across our Development, Analyst, Sales and Marketing departments. With this expansion came an increase in our consumption as well. We have gulped over 11,000 liters of water, 10,300 cups of coffee and over 5,200 cups of tea. Oh and about 600 shakers of whey protein (gains gains gains!!!). Let's not talk about food, we eat a lot…especially when waiting for flights, all 56 of them this year.

We need to fuel ourselves though, this year we have passed the milestone of "220,000 work connections analyzed" with OrgMapper, and our Diktio Labs team was on a roll as well. They have analyzed over 20 million Facebook likes and 38 million Tweets to date. No wonder we need those 7,300 cups of coffee…

If you were to call us all at the same time, you could expect 8 iPhones and 24 Androids to ring, but call us before 7 P.M., otherwise we might be at an ice-hockey match or a pub quiz. Our analyst, Balázs attended 43 ice-hockey games this year and as far as our trivia-fondness goes, our quiz-enthusiasts answered over 2,000 questions (most of them correctly). We are a fun-loving bunch, but we also put great emphasis on supporting charities and other serious causes too, hence the heroes and heroines of the office donated 15 liters of blood this year.

All in all, 2015 was a challenging, but fun year in the life of Maven7 and we can hardly wait for the new year to come with everything it holds for us.

Happy Holidays and see you in 2016!

What the change management community looks like on Twitter - mini research

2015-10-22T16:40:00.000+02:00

Change Management has been amongst the top corporate priorities for some time now, yet it still poses serious challenges to even some of the most flexible and responsive organizations. For Change Management to be effective and successful, it is absolutely essential to identify the most influential members and change agents within an organization's internal stakeholder network. And that is exactly what we do with our cloud-based organizational diagnostic tool, OrgMapper.

This time, however, we wanted to see who these influential key players are online, thus we used Diktio Labs, our online community mapping suite, and conducted a mini research to identify whom the Twitter community considers Key Opinion Leaders (KOLs) when it comes to the topic of change management, and the ideas that connect them.

How we did this

We monitored a specific set of keywords related to Change Management from September 14 to October 7, 2015, and mined over 17,000 tweets, tracking the activities of 9,054 users. The below image showcases the network of key hubs.

Each node (lilac dot) in the above network represents a Twitter account; the more interaction a user gets, the larger their node and label will be. For a better visual experience, the network is filtered to only depict edges (connections) that form amongst people who interacted with each other at least ten times. The thickness of the edges indicates the number of interactions between given users. Arrows indicate the direction of the interaction.

Key findings

Our analysis revealed a list of keywords most frequently used in conversations related to Change Management. On the below chart we filtered out the obvious "Change", "Management", "Organizational" and "#ChangeManagement" keywords in order to provide a more accurate picture of other relevant topics. The results demonstrated that innovation, culture and behavior are often mentioned together with Change Management.

A different approach to visualizing key topics related to Change Management.

Top Key Opinion Leaders in Change Management

In order to be considered a KOL in our mini research, users had to have more than 500 followers, and at least 10 tweets and over 10 incoming interactions during the analyzed time period. To calculate and determine the rankings of Key Opinion Leaders, we have taken into account their:

number of incoming replies, mentions and retweets

number of followers

number of tweets

Based on our analysis, the final list of KOLs in the topic of Change Management is as follows:

As you can see, being an online influencer is not solely about the number of followers a user may have. Our top list includes Twitter accounts with as few as 820 followers all the way to 61,000.

Why influencer marketing is important

Great marketers know and engage their target audience's key influencers. Conducting an online community mapping analysis enables you to identify key opinion leaders who are really capable of getting through to the audience you want to engage, and the main topics that trigger and drive them. Value-driven influencer engagement will not only help strengthen your KOLs advocacy, but also prompt them to get involved in your communication campaigns.

For more information on online influencer identification, contact us at info@diktiolabs.com.

Maven7 Welcomes New Director for Its OrgMapper Business Line

2015-06-30T12:31:00.000+02:00

It is our pleasure to announce the appointment of Hiske den Boer as Director of our OrgMapper business line. Hiske is joining us to oversee service delivery, as well as business- and product development related to organizational network analysis.

"With our recent round of funding we felt the time was just right to appoint a strategic leader who can help us take advantage of the tremendous opportunities in the marketplace, and bring new insights into our operations." – said András Vicsek, Maven7's co-founder and head of research. "We are delighted Hiske accepted our invitation."

"I have been working with Maven7 part-time since last year as a senior business consultant" - commented Hiske, "mostly assisting clients in understanding what benefits organizational network analysis can provide for their organizations. Now I'm looking forward to diving in deeper and making a real impact on business objectives, whilst also growing our team of OrgMapper experts."

Over the past 17 years Hiske has worked for multiple management consulting firms, including Accenture and PricewaterhouseCoopers, leading numerous large-scale HR projects, and is now ready to take OrgMapper forward and further expand its global reach and capabilities.

We are collectively really excited to work with her, and about the new energies, ideas, and skills she brings to the whole Maven7 team.

Welcome on board, Hiske! :)

Maven7 Raises a Total of $2.3M to Bring Network Science to Commercial, Mass Audience

2015-06-08T09:44:00.000+02:00

June 8, 2015, Boston, MA - Budapest, Hungary

Maven7, a leading player in network science-based business analytics, has raised over $1.5 million in 'Series A' funding led by venture capital management firm, Perion. The funding is earmarked for market expansion and the further development of Maven7’s business analytical tools.

"With this investment round, the international sales of our already successful solutions receives yet another boost. After a successful US and Latin American market entry, we are working on acquiring further customers, entering new markets, and introducing new products." said Peter Ruppert, CEO and co-founder of Maven7. "We are proud to have Perion on board, and are excited to move forward with the resources to help make our vision a reality. Our focus is to put more and more powerful tools into the hands of our customers, with which they can quickly and profoundly understand how the complex networks most important to them operate, and thus gain concrete financial benefits." added András Vicsek, co-founder and head of research.

Perion led this round of funding because it believes in Maven7's dynamic growth, and that the startup has a continued potential to become a multi-million dollar company. "We invested in Maven7 for its strength in leveraging and expanding the latest findings in network science, its proven ability to provide immediate business benefits. I am intrigued by what I have seen so far; Maven7 is in a very exciting space, bridging the ideas of enterprise intelligence, big data and social analytics, and can't wait to see how far it will go." explained Gábor Vitán, CEO of Perion.

Maven7 empowers strategic leaders to make better-informed decisions by introducing network science into the business planning process. Via its two distinct product suites, OrgMapper and Diktio Labs, Maven7 provides organizational network analysis to HR practitioners, online community and influencer mapping solutions to marketing professionals, and big data analytics.

Among its co-founders are some of the biggest names in network science, including award winning scientists, Albert-László Barabási and Tamás Vicsek, through whom the Budapest and Boston-based startup also has very close working relations with the prime academic think tanks of the discipline.

Maven7 opened its first overseas office in Boston last year, and already has a prominent presence on the European, Latin American and US markets.

Maven7 raised more than $750,000 from Primus Capital in seed in 2014, putting the current total investment at over $2.3M.

About Perion

Perion is a member of Hungarian Venturio Group, with funds supported by the European investment fund initiative, JEREMIE. Perion aims to invest $16 million in promising Hungarian ventures during its investment phase ending in 2015. With the IT sector being one of its main focus areas, Maven7 is the 4th investment on the venture capital management company's portfolio.

About Maven7

Maven7 supports business decisions by transforming large amounts of hard-to-interpret data into actionable business intelligence. Based on the methodologies of network analysis and data mining, Maven7 has developed its own proprietary network mapping tools, including OrgMapper and Diktio Labs, to conduct analyses in organizational development, social media, the pharmaceutical/medical industries and many other fields.

About Diktio Labs

Diktio Labs takes marketing to a whole new level of targeting and efficiency by enabling organizations to know exactly who will prove receptive, influential and responsive to their messaging prior to a campaign launch. Diktio Labs works directly with end-user clients and also partners with a number of marketing advisors, consultancies and agencies to deliver its technology into a fully integrated campaign.

About OrgMapper

OrgMapper is an easy-to-use cloud-based software application developed to gather, measure and visualize organizational interrelations. Via organizational network analysis (ONA), it maps the human side of organizational processes; it reveals hidden informal employee networks, exposes critical connections, departmental and hierarchical links, uncovers the barriers to information flow, and identifies key individuals who connect departments and products, or are influential to their colleagues.

About Primus Capital

The Primus-managed Venture Capital Fund was launched in 2010 with $28M of core capital. It currently has 14 companies on its portfolio, all of which are built on innovative technologies and sell to international markets. Through the Primus-provided funding and expertise, Maven7 achieved a significant increase in value recently.

Media contacts:

Dávid Kálmán			Zsuzsanna Blau
Senior Analyst, Perion			Head of Marketing and Communications, Maven7
Phone: +36 20 239 0914			Phone: +36 30 210 4830
Email: kalmand@perion.hu			Email: zsuzsanna.blau@maven7.com

How the HR Tech Europe conference went down on Twitter

2015-03-27T13:59:00.001+01:00

HR Tech Europe, the self-proclaimed fastest growing HR event in Europe, took place on March 24-25 in London. The conference drew HR practitioners from far and wide to discover the latest trends in HR technology. Although we could not be there in body to showcase our cloud-based organizational diagnostic tool, OrgMapper, we wanted to show some spirit and decided to map the social activity around the event.

How we did this:

In order to map the structure of the online community around the conference, we tracked Twitter with our online community mapping suite, Diktio Labs, between 6 am, March 18, 2015 and 11:50 pm March 26, 2015, and looked into the network of all relevant interactions. We downloaded 11,013 tweets containing the hashtag #HRTechEurope.

Each node (blue dot) in the below network represents a Twitter account; the more influential a user is, the darker and larger its node will be. For a better visual experience, the network is filtered to only depict edges (connections) that form amongst people who retweeted each other at least three times. The thickness of the edges indicates the number of retweets; meaning, the thicker the edge, the more often a user retweeted another user. Arrows indicate the direction of the retweet: the head of the arrow points at the retweeted user.

Click here to enlarge

Our key findings:

@hrzone was the most retweeted user with 493 retweets, followed by @oraclehcm, @let_anita, @david_green_uk, @mervyndinnen, and @rohitbhosale. The network is well-connected, though @rohitbhosale and @emee_insights form almost a separate cluster but still connected via @violazoldy.

We also took a peek at the most trending hashtags, but excluded #HRTechEurope (which was used extensively throughout this period). We found that #HR was the most widely used hashtag, with 605 mentions within the community, followed by the hashtags #ngarh and #Gamification.

In 2015 online influencers can make or break a brand. Instead of concentrating on HOW MANY people are talking about your business, focus on WHO is in the middle of these discussions. Great marketers know and engage their social media influencers. Building close relationships with your influencers will not only help strengthen their advocacy, but also convince them to get involved in your communication campaigns.

For more information on online influencer identification, contact us at info@diktiolabs.com.

Maven7 Introduces Dedicated Brand for Community and Influencer Mapping

2014-07-16T13:47:00.000+02:00

Diktio Labs aims to transform the marketing sector by bringing network science to the fingertips of all marketing professionals

Maven Seven Inc. (Maven7), one of the world's leading network research and data mining companies, has announced the introduction of Diktio Labs™, its new suite of online community and influencer mapping solutions.

Developed to empower marketing professionals by integrating network science into the campaign planning process, Diktio Labs is set to transform the digital marketing sector. For the first time, any proactive informational outreach campaign will have at its disposal a range of innovative and cost-effective planning tools driven by powerful network science.

Diktio Labs' data-driven approach stands in stark contrast to commonly used volume-based monitoring tools and methods. Traditional tools track campaign outputs, monitoring sentiment, conversations, trends and behavior. By contrast, Diktio Labs aims to map networks, hidden structures, relationships and both individual and group behavior across a range of client-specific criteria (issues, behavior, beliefs, preferences, patterns, etc.). Its algorithms and scrapers are tailor-made to each campaign, offering insight and intelligence to a level never before possible.

"Modern companies have to understand better how their customers seek out information online," said Chris Dobson, a seasoned communications agency manager who is driving Diktio Labs' to-market strategy.

"A huge amount of money is wasted through trial and error within digital and, indeed, traditional marketing, mostly completely unnecessarily. We now have the technology to identify communities of interest by a myriad of criteria and help transform any outreach campaign, be it political-, marketing- or social-based, in terms of insight and accuracy. Anyone in these areas not embracing network science now will soon be left behind."

A co-founder of Maven7 is the globally-acclaimed pioneer of network theory, Albert-László Barabási, based at Northeastern University in Boston. He also believes that the time has come for all marketing to take full advantage of network science: "We are constantly surrounded by hopelessly complex systems, from our society to computers and cell phones, to even the networks of the billions of neurons in our brains. These systems, although random looking at first, display endless signatures of order and self-organization, which can be understood, quantified, predicted, and eventually controlled. Show me a marketer that wouldn't want to take advantage of these advances to exploit that information."

Diktio Labs' products are available in almost all languages, and have already been used across over 15 countries, as diverse as Columbia, China, Mexico, Russia, Hungary, the UK and the US. Tailor-made algorithms specific to each client and project guarantee accuracy and relevance levels far beyond 'off the shelf' models.

About Maven7
Maven7 supports business decisions by transforming large amounts of hard-to-interpret data into actionable business intelligence. Based on the methodologies of network analysis and data mining, Maven7 has developed its own proprietary network mapping tools to conduct analyses in organizational development, social media, the pharmaceutical/medical industries and many other fields.

About Diktio Labs
Diktio Labs takes marketing to a whole new level of targeting and efficiency by enabling organizations to know exactly who will prove receptive, influential and responsive to their messaging prior to a campaign launch. Diktio Labs works directly with end-user clients and also partners with a number of marketing advisors and agencies to deliver its technology into a fully integrated campaign.

Fans voted the US and Iran the most disliked World Cup teams

2014-06-12T22:20:00.001+02:00

According to a 19-country study conducted by YouGov and Upshot, the good ol' US of A and Iran are the least favored teams in the world's favorite sporting event, the World Cup, starting these very minutes.

We thought that the question "Which Team Are You Rooting Against?" deserved our full attention, so we took to analyzing the results and drew up a pretty network map. Read this New York Times article for more information regarding the outcomes of this question (including the seemingly obvious ties between soccer and politics) and others.

Mexican politics in the social space

2014-03-26T17:05:00.000+01:00

What does the relationship between political parties look like in Mexico when it comes to online audiences? We wanted to answer this question, therefore we took to analyzing the Facebook activities of various fanpages for the month of January – just like we did earlier this month with Colombian online politics.

The analysis looked into the online dynamics between and amongst the registered political parties in Mexico. Only fanpages were considered, user or group accounts on Facebook were ignored.

The examined data covered:

12 356 posts;
239 884 users who liked posts;
18 972 users who commented on posts.

In order to reveal the dynamics amongst and between political parties, we have included the aggregate network of parties indicating the structure of their overlapping audiences, the timeline exposing the activity of their supporters, and a cross-over audience analysis for each analyzed political page.

Click here to download the full January Facebook analysis of Mexican political parties.

Our Colombian online politics analysis made the front page of La Republica

2014-03-10T17:03:00.002+01:00

What is the relationship between political parties in Colombia in terms of online audience?
This was the first and foremost question of our mini-research.

Our analysis looked into the dynamics of online political audiences in Colombia by examining the January 2014 activities of 118 Facebook fanpages of various political parties. The aim was not to provide a representative analysis, similar to that of the offline polls, but to offer a glimpse into of the different supporters of these parties.

We've only considered fanpages in the study and ignored user or group accounts. The examined data covered 5 835 posts, 60 067 users who liked posts and 4 947 users who commented on posts.

The analysis includes the aggregate network of parties revealing the hidden structures of online supporters between them; the timeline of parties indicating the activity of their supporters; and a cross-over audience analysis for each analyzed political page.

Click here to download the full Colombian online politics analysis.

The first autonomous drones that flock like birds are Hungarian!

2014-02-28T11:47:00.000+01:00

Our cofounder and Academic Director, Tamás Vicsek, has made it to Nature Magazine again with an absolutely fascinating project: the creation of autonomous drones that flock like birds.

Tamás Vicsek

He coordinates the COLLMOT Robotic Research Project, a five-year program on the complex structure and dynamics of collective motion, funded by EU ERC.

Based on their findings on the collective behavior of birds such as pigeons, his team of Hungarian researchers created 10 self-organizing drones that follow rules of collective motion. "We came to the conclusion that one of the best ways to understand how animals move together is to build robots - flying robots", said Vicsek.

Equipped with GPS trackers and radios that allow them to navigate in formation, the quadcopters were tested in open-field environment for maximum authenticity. Instead of a central control, the drones use a flocking algorithm when their flying path becomes jammed, and wait for their turn while hovering in place.

Watch the video to see them in action:

FirmNet Online rebranded to OrgMapper

2014-02-21T16:54:00.001+01:00

Our online organizational diagnostic tool, FirmNet Online, has undergone a rebranding, and will now officially become OrgMapper.

The new name focuses on the essence of the solution at hand, and crystallizes in a single word the underlying methodology our product offers.

Visit orgmapper.com to see the rebranding for yourself.

Primus Capital invests in the expansion of Maven7

2014-02-20T15:49:00.002+01:00

Official press release on the recent happenings in our company's life:

Budapest, Hungary – Boston, MA
Primus Capital Fund Management Ltd. is investing more than 170 million forints (approx. 750,000 US dollars) acquiring a minority stake in Maven Seven Network Research Ltd. (Maven7), a provider of unique network research-based analyses and services. Through the capital investment, the 4-year-old company will further develop its online diagnostic tool, OrgMapper, and take it to Western European and overseas markets. It will also strengthen the sales activities of its US subsidiary and further develop its network analysis services for the media and pharmaceutical sectors.

Maven7 applies the latest results and innovations in network science to the business environment. Among its co-founders are such internationally acclaimed scientists of the discipline as Albert-László Barabási (Northeastern and Harvard Universities) and Tamás Vicsek (Hungarian Academy of Sciences, ELTE), who rightfully ensure the company's academic credibility.

Maven7 supports business decisions by transforming large amounts of hard-to-interpret data into actionable business intelligence.

The company's online Software as a Service (SaaS) platform, OrgMapper, allows its users, primarily in the business consulting sector, to conduct analyses in organizational development, the media, and the pharmaceutical industry, on the basis of which consultancies can provide M&A advisory, change management and other services to their customers.

Maven7 is the 10th investment of Primus Capital. "International competitiveness and execution built on the expertise of Central European physicists and engineers – this is the strategy of Maven7 and Primus Capital. We are proud to be working with such a fast-growing company and a team of extremely talented professionals in order to achieve international growth", added Zoltán Bruckner, investment director at Primus Capital.

Is football really a simple game?! The hidden networks behind Bayern's success!

2013-05-27T16:47:00.002+02:00

The infographic was created by Avalanche. CLICK FOR FULL SIZE

With the power of network visualization, dynamics of football games can be understood better than ever. Maven7’s analyst team is a huge fan of sports (check out our last analysis about the chances of the Hungarian water-polo team at London Olympics), especially football.

As everybody knows it, "football is a simple game; 22 men chase a ball for 90 minutes and at the end, the Germans always win". So then why do so many people admire this simple form of entertainment? Why do dozens of analysts try to predict who will win a certain game or championship? Why is betting a huge business? The answer is as simple as football, because this game is not simple at all! Behind every pass, attack and goal, human dynamics have a strong impact. Network Analysis can give a new approach to understanding team dynamics during football games.

Our recent infographic shows the hidden networks of two finalists of Champions League’s 2013. Let’s face the big question; can network science provide the answer why Bayern won and not Dortmund?

If you look at the pictures, similarities and differences are easily noticeable. Network structures and patterns resemble each other because of the same line-up structure. Two defenders (greens) had strong mutual pass connections at both teams, but Dortmund focused on the right and Bayern on the left back. Teams have preferred defensive midfielders - Schweinsteiger and Gündogan, they were the top choice to pass to in midfield. OK, so both teams are German and both have same line-ups, but what isthe difference then?

Why did Bayern win?

Dortmund’s midfielder, Reus was the preferred player to pass to from the attacking midfielders. The penalty that Dortmund received also came from a situation after a pass to Reus.

At the attacking midfield, Bayern is more active on the wings, and their whole network is not that centralized as Dortmund’s. Bayern’s midfield played in a better cooperation; their network shows more mutual connections, and Ribery’s supportive role on the left wing makes the whole attacking part very successful. Unfortunately, Dortmund’s attacking midfield has no mutual connection, and the whole midfield has only one as well. In comparison; Bayern’s attacking midfield has mutual connection between Robben and Ribery, and the midfield also has 3 mutual connections (Schweinsteiger - Ribery, Müller – Robben, Ribery – Martinez), which may show stronger cohesion in the midfield.

Also, the midfield players’ performance of the two teams indicates their teams’ performance. Schweinsteiger played and passed more actively and punctual (87 tries, 73 times successful – 84%) than Gündonan (56 tries, 31 times successful – 62%), and while Bayern had altogether 640 passes and their efficiency was 72%, Dortmund had only 448 passes with 60% efficiency.

An interesting fact is, that those attacks, which started from the goalkeeper, are more likely happening by the players of Dortmund. In general, Dortmund’s defense played a more attacking role; while Dante passed mostly to the back, Boateng passed to the front.

Maven7

The Harlem Shake Story - aka. Birth of a Meme

2013-03-18T13:59:00.000+01:00

If you still have not heard of the Harlem Shake you must be living in a cave. Much has been written about the rapid and global spread of this catchy internet meme, yet little is understood about how it spread. A series of remixed videos along with a number of key communities around the world triggered a rapid escalation, giving the meme widespread global visibility. Who were the initial communities behind this mega-trend? SocialFlow took a look at 1.9 million tweets during a two-week period that included the words ’harlem shake’, or some versions of it.

The Harlem Shake itself is a dance style born in New York City more than 30 years ago. During halftime at street ball games held in Rucker Park, a skinny man known in the neighborhood as Al. B. would entertain the crowd with his own brand of moves, a dance that around Harlem became known as 'The Al. B. Though it started in 1981, the Harlem Shake became mainstream in 2001 when G. Dep featured the dance in his music video "Let's GetIt". While mining Twitter data, references to Harlem Shake (the original dance) were seen quite often prior to it becoming a popular meme. When someone tweets, "I just passed my final exams! *harlem shakes*," it's the equivalent of saying "I just passed my final exams! Look at me dancing!" While Bauuer's now infamous track was released on Diplo's Mad Decent label back in August 2012 (posted to YouTube on August 23 2012), it only accrued minor visibility for the first few months. Then February hit, and something changed.

The timeline below highlights the very first days as the meme was taking off. In blue, we see references to the 1980's dance harlem shakes, while the green curve represents Tweets that use the phrase 'The Harlem Shake', many of them linking to one of the first three versions of the meme on YouTube.

On February 2, The Sunny Coast Skate (TSCS) group establish the form of the meme in a YouTube video they upload. On the 5, PHL_On_NAN posts a remix (v2), gaining 300,000 views within 24 hours, and prompting further parodies shortly after. On Feb. 7, YouTuber hiimrawn uploaded a version titled "Harlem Shake v3 (office edition)" featuring the staff of online video production company Maker Studios. The video becomes is a hit, amassing more than 7.4 million views over the following week, and inspiring a number of contributions from well-known Internet companies, including BuzzFeed, CollegeHumor, Vimeo and Facebook.

Social Flow looked at the social connections amongst users who were posting to the meme. This gave them the ability to identify the underlying communities engaging with the meme at a very early stage. In the graph above each node represents a user that was actively posting and referencing the Harlem Shake meme on Feb 7 or 8 to Twitter. Connections between users reflect follow/friendship relationships. The graph is organized using a force directed algorithm, and colored based on modularity, highlighting dominant clusters - regions in the graph which are much more interconnected. These clusters represent groups of users who tend to have some attribute in common. The purple region in the graph (left side) represents African American Twitter users who are referencing Harlem Shake in its original context. There's very little density there as it is not really a tight-knit community, but rather a segment of users who are culturally aligned, and are clearly much more interconnected amongst themselves than with other groups.

After a similar analysis on the following two days (Feb 9 and 10) different communities can be seen emerging, resulting in a much more tightly knit graph structure. While the same dense cluster of musicians and DJs (in turquoise) still exists, there are substantially more self-identified YouTubers both across the US and the UK. At the same time there's a significant gamer / machinima cluster that's also participating, as well as a growing Jamaican contingent, and quite a few dutch profiles (purple -- left). Additionally, we see various celebrity and media accounts who caught on to the meme -- @jimmyfallon, @mashable and @huffingtonpost. By capturing the two snapshots, we can also make sense of the evolution of the meme as it becomes more and more visible. At first, loosely connected communities separately humored by the videos. Within days, we see major media outlets jump on board, and a much more intertwined landscape. We see different regions in the world light up, and identify communities of important YouTube enthusiasts who effectively get this content to spread.

Memes have become a sort of distributed mass spectacle, a mechanism that both capture people's attention, and define what is "cool" or "trendy." We see more and more companies and brands try to associate themselves with certain memes, as a way to maintain a connection with their audience, gain the cool factor. Pepsi did this with the Harlem Shake and saw an incredibly positive response.

As we get better at identifying these trends and trend-setting communities early on, the pressure to participate will rise. As social networks become globally-intertwined, we're witnessing a growing number of memes conquer the world at large. These moments are critical points in time, where there are significant levels of attention given towards a specific entity - be it a joke, funny video or a political topic. Piecing together data from social networks can help us identify critical points in time, as well as the underlying communities and trendsetters for the humor-based memes, or the agenda setters for politically-slanted ones. The only question is: what will be the next one, cashing in on it 15 minutes?

Hungry for more? Read the full article on HuffPost.

Networking autism

2013-03-05T16:33:00.001+01:00

A new American study, using network analysis may help in understanding some classic behaviors in autism.

A look at how the brain processes information finds a distinct pattern in children with autism spectrum disorders. Using EEGs to track the brain’s electrical cross-talk, researchers from Boston Children’s Hospital have found a structural difference in brain connections. Compared with neurotypical children, those with autism have multiple redundant connections between neighboring brain areas at the expense of long-distance links.

Peters, Taquet and senior authors Simon Warfield, PhD, of the Computational Radiology Laboratory and Mustafa Sahin, MD, PhD, of Neurology, analyzed EEG recordings from two groups of autistic children: 16 children with classic autism, and 14 children whose autism is part of a genetic syndrome known as tuberous sclerosis complex (TSC). They compared these readings with EEGs from two control groups—46 healthy neurotypical children and 29 children with TSC but not autism. In both groups with autism, there were more short-range connections within different brain region, but fewer connections linking far-flung areas. A brain network that favors short-range over long-range connections seems to be consistent with autism’s classic cognitive profile—a child who excels at specific, focused tasks like memorizing streets, but who cannot integrate information across different brain areas into higher-order concepts. For example, a child with autism may not understand why a face looks really angry, because his visual brain centers and emotional brain centers have less cross-talk. The brain cannot integrate these areas. It’s doing a lot with the information locally, but it’s not sending it out to the rest of the brain.

The most popular autistic character of the silver screen is Raymond Babbitt from Rain Man, a true savant with amazing memory and mathematical skills, but an incapability to change adaption. A cinematic fun fact: the real life human being and inspiration to his character Kim Peek was suffering from another disorder than autism.

Network analysis—a hot emerging branch of cognitive neuroscience—showed a quality called “resilience” in the children with autism—the ability to find multiple ways to get from point A to point B through redundant pathways. Much like you can still travel from Boston to Brussels even if London Heathrow is shut down, by going through New York’s JFK airport for example, information can continue to be transferred between two regions of the brain of children with autism. In such a network, no hub plays a specific role, and traffic may flow along many redundant routes. It’s a simpler, less specialized network that’s more rigid, less able to respond to stimulation from the environment.

Do we have your curiosity and your attention? Read more on Psypost.

Networks of Marvel Heroes - INFOGRAPHIC

2013-02-27T14:33:00.000+01:00

Masked vigilantes seem to be quite the socialites, when it comes to the company thy keep – or are they? Find out for yourself by taking a closer look at the graphic version of the Marvel Universe’s own social network. All characters with at least 100 mutual inked appearances are present, coloured according to their team or universe affiliations. For a more detailed analysis, follow the link, for all the fans who already know all there is to know: enjoy the visual feast!

Inaugural Networks

2013-02-19T13:33:00.000+01:00

Presidential inaugural speeches in the US provide a good indication of the forthcoming political agenda. There has been a lot of research dedicated to this subject, however most of it focuses on keyword frequency analysis, which makes it difficult to trace the change in political agenda over the years. The reason is that the public political discourse is quite predictably dominated with such notions as “people”, “nation”, “world”. What’s interesting, however, is to detect the moments when the new notions are introduced into the political agenda, as well as to trace the change in relationships between the terms. This is where text network analysis can be quite useful, so Nodus Labs created a special report for The Guardian newspaper based on the US presidents inauguration speeches from Nixon’s 1969 to Obama’s 2013 address.

The analysis used the method for text network analysis. The basic premise of this approach is that every word is represented as a node and their co-occurrence within the same context is represented as an edge in the network. After a series of transformations (performed by Textexture software developed by Nodus Labs) the graph is produced, which is then aligned according to Force Atlas algorithm. The nodes (words) that are connected (co-occur within the same context) are pulled together, while the nodes that are not connected are pushed. The resulting aligned graph gives a very good representation of the major semantic fields present within the text. Furthermore, community detection algorithms are applied to the resulting network, sorting the nodes (words) into the different groups according to how interconnected they are to one another. Every community is represented with a different color. As a result, if two words co-occur often together inside the same text they will be positioned next to each other on the graph and also belong to the same community (and, thus, have the same color on the graph). These communities represent the topics inside the text. Finally, the nodes are ranked according to their betweenness centrality measure: the bigger the node, the more different communities it belongs to.

It’s worth noting that such approach is very different from so-called “tag clouds”. Tag clouds show the most frequently mentioned words and they rarely position these words according to their proximity within the text. Therefore, one can get a general idea of the vocabulary inside the text, but it’s very hard to have a sense of the meaning that is produced using this vocabulary. Text network visualization, on the other hand, emphasizes both the most frequently mentioned words, as well as the relationships between them, making it much easier to understand what the text is about. Furthermore, it can also detect the topics inside the text, making it a much more useful tool for improving text comprehension and providing a much more useable interface for text navigation.

Bush, 2001:

Bush, 2005:

Quite a generic agenda at first sight, however, Bush was the first one to introduce the notion of “time” and use it to motivate certain policies. It’s all about the Now: “In all of these ways, I will bring the values of our history to the care of our times.” Not surprising that the “story” is also such an important concept in his speech: it’s full of short stories. In 2005, after the re-election is over, Bush is running the second term, probably thanks to his emphasis on “freedom” and “liberty” – a trick that always worked in the US and that was successfully employed by Reagan in his second term (see above).

Obama, 2009:

Obama, 2013:

The master of rhetorics, Obama combines the best of his predecessors in this inauguration speech. No wonder the “word” has such high relevance in his speech – it refers to the moments Obama is quoting someone else. In 2013’s speech the “time” and “require” probably relates to the fact that Obama had to respond to all the criticism that something had to be done immediately about the state of US economy and politics – and he successfully addressed these concerns.

Source:

http://noduslabs.com/cases/presidents-inaugural-speeches-text-network-analysis/

See more, about the text network analysis:
http://noduslabs.com/research/pathways-meaning-circulation-text-network-analysis/

Connecting the Community

2013-02-15T16:37:00.000+01:00

We all live in multiple on-line communities, but what do these communities look like? Where are we located in each of our communities, and what role do we play?

The diagram below shows an actual on-line community [OLC]. Every node in the network represents a person. A link between two nodes reveals a relationship or connection between two people in the community -- the social network. Most on-line communities consist of three social rings -- a densely connected core in the center, loosely connected fragments in the second ring, and an outer ring of disconnected nodes, commonly known as lurkers. Communities have various levels of belonging -- each represented by one of these rings. You may belong in the core of one community, while being a peripheral lurker in another.

In the above diagram, we see three distinct types of membership in our community -- designated by blue, green and red nodes. The proportion of nodes in each ring in this population is fairly typical of most on-line communities -- the isolates [lurkers] outnumber the highly-connected by a large ratio. The outer orbit in the network above contains the blue nodes. They have been attracted to the OLC, but have not connected yet. The blue nodes contain both brand new members, who have not connected yet, and passive members who have seen no reason to connect. The passive group is the most likely to leave the OLC, or remain as absorbers-only of the content in the community.

The green nodes have a few connections -- usually with prior acquaintances. They are not connected to the larger community -- only to a small, local group. They do not feel a sense of true membership in the larger whole, though they may identify with it. The small clusters of friendships amongst the greens can be maintained by other media and do not need this particular OLC to survive. They are also likely to leave or become passive and will likely do so in unison with the rest of their small circle of friends.

The inner core of the community is composed of red nodes [zoomed-in view below]. They are very involved in the community, and have formed a connected cluster of multiple overlapping ego networks. The leaders of the OLC are embedded in this core cluster. The core members will stay and build the community. Unfortunately they are in the minority. The core node consists of usually less than 10% of most on-line groups -- sometimes they are as few as 1% of the total OLC. Although small, they are a powerful force of attraction. It is the core that is committed and loyal to the OLC and will work on making it a success.

Online communities and social networks are often conceived and developed by businesses and organizations that focus on: "How can we use the online community to benefit us?" Focusing only on how to utilize the community, leads many organization to failure in building these communities! They fail at community development by not creating a strategy that makes sure their target audience is gaining a positive experience and practical benefits from participating in the community. It is amazing how many organizations try to build on-line social networks while ignoring the needs of the very people they are trying to attract and influence! It is then no surprise when large chunks of their target group leave when the "next big thing" comes around: SixDegrees-->Friendster-->Orkut-->MySpace-->Facebook-->Next? To build a vibrant and growing OLC, you need to support natural human behavior, not work against it. You need to think sociology, not just technology.

The field of social network analysis [SNA] gives us tools to both know the net and knit the net. SNA maps and measures the paths of information, ideas and influence in the community. SNA reveals the emergent patterns of interaction in organizations and communities and allows us to track their changes over time.Growing a community is not just adding new members. It requires adding both people and relationships -- nodes and links. Node counts are important in social networks, but it's the relationships -- and the patterns they create -- that are key! A community thrives by its connections, not by its collections! It's the relationships, and the prospect of future relationships, that keep members active and excited.

Source: http://orgnet.com/community.html

Manchester City vs Liverpool: Passing network analysis

2013-02-13T15:51:00.000+01:00

At the beginning of February, Manchester City drew 2-2 with Liverpool at the Etihad, so a football loving blog decided to take a look at the match from a network point of view, resulting in the following research. We have already reported about something similar regarding basketball.

The positions of the players are loosely based on the formations played by the two teams, although some creative license is employed for clarity. It is important to note that these are fixed positions, which will not always be representative of where a player passed/received the ball. Only the starting eleven is shown on the pitch, as the substitutes weren’t hugely interesting from a passing perspective in this instance. Only completed passes are shown. Darker and thicker arrows indicate more passes between each player. The player markers are sized according to their passing influence, the larger the marker, the greater their influence. The size and colour of the markers is relative to the players on their own team i.e. they are on different scales for each team.

In the reverse fixture, Yaya Touré and De Jong were very influential for City but Touré was away at the African Cup of Nations, while De Jong joined Milan shortly after that fixture. Their replacements in this game, Barry and Garcia, were less influential, although Barry had the strongest passing influence for City in this match, with Milner second. The central midfield two, Lucas and Gerrard, were very influential for Liverpool and strongly dictated the passing patterns of the team. They both linked well with the fullbacks and wider players, while Lucas also had strong links with Suárez and Sturridge. Certainly in this area of the pitch, Liverpool had the upper hand over City and this provided a solid base for Liverpool in the match.

Similarly to the Arsenal game, Liverpool showed less of an emphasis upon recycling the ball in deeper areas. Instead, they favoured moving the ball forward more directly, with Enrique often being an outlet for this via Reina and Agger. Liverpool’s fullbacks combined well with their respective wide-players, while also being strong options for Lucas and Gerrard. Strurridge was generally excellent in this match and was more influential in terms of passing than in his previous games against Norwich and Arsenal, combining well with Suárez, Lucas and Gerrard.

At least based on the past few games, Liverpool have shown the ability to alter their passing approach with a heavily possession orientated game against Norwich, followed up by more direct counter-attacking performances against Arsenal and Manchester City. The game against City was particularly impressive as this was mixed in with some good control in midfield via Lucas and Gerrard, which was absent against Arsenal. How this progresses during Liverpool’s next run of fixtures will be something to look out for.

Source: http://2plus2equals11.wordpress.com/2013/02/05/manchester-city-vs-liverpool-passing-network-analysis/

Basketball Isn’t a Sport. It’s a Statistical Network

2013-02-05T10:21:00.002+01:00

Team sports and statistics are no strangers, take sabermetrics, that revolutionized game analysis for baseball, while making it more fun to watch. The story might sound familiar if you have seen Moneyball, where Brad Pitt took on the role of Billy Beane, who pumped up the game of the Oakland A’s.

Compared to baseball, though, basketball is much more dynamic, and ball movement becomes a key variable in success. Passing is one of the fundamentals of hoops, and in the upper ranks of the sport, turnovers — often the result of wayward passes — contribute to ticks in the win-loss column. Fast, agile passing can make or break a team. That’s why sabermetrics might not tell the entire story about what happens on the court. Researchers at Arizona State University, led by life science professor and basketball fan Jennifer Fewell and math professor Dieter Armbruster found an ideal model to explain the results of the 2010 NBA playoffs by simply keeping their eye on the ball. Their work opens the door to an entirely new line of sports analysis, from game-tape breakdown to highlight reels and augmented-reality visualizations.

Their method - not surprisingly – was network analysis, which turns teammates into nodes and exchanges — passes — into paths. From there, they created a flowchart of sorts that showed ball movement, mapping game progression pass by pass: Every time one player sent the ball to another, the flowchart lines accumulated, creating larger and larger and arrows. Using data from the 2010 playoffs, Fewell and Armbruster’s team mapped the ball movement of every play. Using the most frequent transactions — the inbound pass to shot-on-basket — they analyzed the typical paths the ball took around the court.

Network analysis of the Chicago Bulls, showing the majority of ball interaction remained with the point guard. Image:

Network analysis of the Los Angeles Lakers shows the team is far more likely to distribute the ball among more players, using the “triangle offense.”

For most teams, the inbound pass went primarily to the point guard, generally a team’s best ball handler. But point guard-centric, such as the Bulls, didn’t fare well in the 2010 playoffs, the researchers told Wired. On the other hand, the Los Angeles Lakers — which won the 2010 NBA championship — distributed the ball more evenly than their rivals, embracing what Phil Jackson calls the “triangle offense,” a technique pioneered by Hall of Fame coach Sam Barry. The basic idea is simple: Maintain balanced court spacing so any player can pass to another at any point.In their model, Fewell and Armbruster found a mathematical explanation for why the triangle offense works — the point guard was no longer the only player feeding passes to fellow players; his teammates were just as likely to take on that role. With more potential passers, there are more potential paths for the opposition to defend.

To quantify their results, published in the journal PLOS ONE, the researchers derived the entropy, or measure of system disorder, for each team during each game. In six of the eight first rounds, winners had higher team entropy, and therefore more randomness, than losers. Though the sample size of teams in the NBA playoffs may be small, the data suggest a possible relationship between quick, unpredictable ball movement and success in games.

While fans direct cheers that fill sports arenas toward athletic giants such as LeBron James or Kobe Bryant, bright statisticians still sit in the shadows. But when these mathematical stars begin helping LeBron improve his game, it’s certain they’ll hear more and more of the applause.

Hungry for more? Read the full article on Wired.

FirmnetOnline training for Consultants

2013-01-14T18:22:00.004+01:00

13 NGOs applied for Maven7’s probono organizational development training, giving them an opportunity, to be consulted by professionals on networking and structural matters. We would like to take the opportunity to thank all the candidates, and congratualte the final three: Greenpeace Hungary, Blue Point Drug Consultation Center and Ambulance and Transparency International Hungary.

During this Firmnet Online training, participants recieved a full network analysis from goal specifications to result analysis. On the first meeting representatives of the third sector and the trainers discussed the problems each of them is facing. By the end of the training, these consultants will be able tor un a full analysis from beginning to end all by themselves.

For more about FirmnetOnline and training opportunities follow this link. Stay tuned for further details on the NGO program.

Zsolt Szegfalvi (Greenpeace) with the consultants of the training.

Social Media and the Power of Networks 2. – Key Opinion Leaders on Twitter

2013-01-03T20:24:00.004+01:00

The increasing impact of social media gives modern marketing a lot to think about; Facebook, Twitter, Tumblr, Flickr, Pinterest, Google+ and hundreds of blogs are only the tip of the iceberg, and it seems impossible to be up-to-date on all the channels. To look at them one by one seems illogical, since the key aspect of the generated content lays in the network effect, that enables the vast exchange of information. What remains to be done? This three-part series introduces Maven7’s newest research focusing on the network effect, and therefore making life easier for online marketing, PR, and product management experts.

In contrast to the Facebook-boom that began 2-3 years ago, and reached it’s 3 million user population in Hungary last year, the Twitter community seems to be growing at a slower pace. The Twitter company was launched in 2006 in San Franscisco, and has around 30 thousand Hungarian visitors a day, similar to the blog hosting site Tumblr.

Why bother with them at all – you may ask? The majority of Twitter and Tumblr users come from an urban environment, most of them are high-status people living in Budapest. Microblogs spread information – especially negative ones – very fast. Here is a comparison: a „tradiotional” online medium might be busy with a story for a whole week, whereas on Twitter – given that the right person spreads it – the same information is distributed within 2.5 hours! Therefore it is of great importance, to keep these outlets under control as much as possible. It is not a coincidence, that Hollywood celebrities like Charlie Sheen (with his 7.5 million followers) get paid around 50thousand dollars per tweet. Our survey conducted during Spanish election season showed that even an average person can have substantial effect on voters. This leaves no second thoughts about monitoring the information that gets to these loyal, high presitge consumers.

National key opinion leaders (famous journalists, bloggers, athletes) are active on multiple scial media platforms, but the small number of follower bases point to the fact, that the person with the most followers is not neccesary the most influental one, when it comes to information distribution. We need to find out, which tweeter is the most relevant one, and has the power to form opinions when it comes to our products. We can achive this through Twitter data using the methods of data mining. The user’s position in the network is another key factor (i.e. how many followers does the user have in common with our competing brand). Compared to Twitter, Facebook has open activity data, which means that we can easily access information regarding the users network of contacts.

Social Media and the Power of Networks 2. – Key Opinion Leaders

There are multiple ways we can build networks from the connections of Twitter users. First of all we can regard the distributors (people related to the brand, or the brand’s official page) as the source of information, and link individual users to them, based on who retweeted the source’s message. Furthermore, the users themselves have followers and friends online, the latter one representing a stronger status, that can be interpreted as a network itself (for more, check our previous article on a follower- andfriend-based network). The picture shows a network of retweeted messages related to an FMCG product distributor and its competitors.

Social Media and the Power of Networks 2. – Key Opinion Leaders on Twitter pic 2.

The second picture represents the choice between data sources, that have the most influence on our consumer basis. The yellow boxes are the key opinion leaders(KOLs), who can reach out to the major part of the community in only three steps. They hold a central position in the network, because they have the biggest follower- and friendbasis.

Through analysis of Twitter data we can not only locate the key opinion leaders and characters of a brand, but with the help of location information we can also interpret product placement related research. A good example of using location data is our previous article on the optimallocalization af ATMs.

To be continued.

The Secret Ingredient of the World’s Best Apple Pie

2012-12-13T15:23:00.000+01:00

Algorythms can predict the future, but unlike the Delphi oracle, they do it based on hard scientific methods, instead of intestines and animal bones. Finding old friends on Facebook, recomending books you might like on Amazon, or predicting the outcome of the 2012 presidential election – you name it, an algorythm does it.

So what about food? Could these same math whizzes help us bake a better pumpkin pie or mix up a tastier batch of sweet potatoes this Christmas? Lada Adamic, a computer scientist at the University of Michigan and Facebook, thinks it just might be possible. she and her team have come up with an algorithm to guess how successful a recipe will turn out. And the math works surprisingly well. It predicts with nearly 80 percent accuracy how many stars your mother's cranberry recipe will receive on allrecipes.com. Plus, it can recommend ingredient replacements to make your pie crust and potatoes more healthful.

She and her team took nearly 50,000 recipes and 2 million reviews from allrecipes.com and then hacked up an algorithm to extract out all the ingredients, cooking methods and nutritional profiles. With just these items, her algorithm could predict the recipe's rating with an accuracy of about 70 percent. But the magic happened when Adamic built a "social network" for the ingredients. She looked at how often two ingredients appear in the same recipes. Those that frequently show up together — milk and butter, nutmeg and cinnamon, basil and rosemary — sit close to each other in the network, but those that rarely appear in the same dish, such as coconut and parsley, are far from each other.

Physicists at Harvard University performed a similar network analysis on ingredients' flavors, but Adamic took it a step further and integrated the data into a recipe prediction program.

Adamic's network analysis boosted the accuracy of her recipe recommendations by about 10 percent. But it also revealed a treasure-trove of information about the way Americans mix and match ingredients, which ones we like to leave out or throw in extra.
Her algorithm analyzed reviewers' recommendations for customizing recipes, such as "I replaced the butter in the frosting by sour cream, just to soothe my conscience about all the fatty calories" and "This is a great recipe, but using fresh tomatoes only adds a few minutes to the prep time." Then the mathematics stitched together little clusters or communities of interchangeable foods and spices.

The result is a list of recipe replacements more comprehensive and scientifically accurate than anything you'll find in the Joy of Cooking or online.

Read the full article on arXiv.com, or a lighter verison on the npr blog.

Network Science of the Game of Go

2012-12-10T13:12:00.000+01:00

You can make networks from pretty much anything. Connect music based on taste or phone calls, companies based on their ownership, spread routes of abstract movements, and lots more. It is high time to start using networks to understand games. But what of the structure of games themselves? In a paper that was recently published in EurophysicsLetters, two French scientists decided to apply network science to the game of Go.

They constructed their networks in a simple way: If one board position can lead to another, they are connected. Using a dataset of about 1,000 professional games and 4,000 amateur games, they began to construct these networks.

In a Game of Go players put black and white stones on a grid board.

Of course, the Go board is very large and so you can’t compare entire board layouts. Instead, they decided to make it much more tractable and look at the board composition surrounding a newly placed piece (a move in Go consists of putting a stone on an intersection of the grid lines of the board). In this case, they looked at the pieces immediately surrounding a newly placed piece (for a 3×3 grid). They calculated that this creates 1107 possible moves, which can be connected if the moves occur one after another, and are in the same region of the board. They also examined the frequency of moves, which obeys a heavy-tailed distribution (whether or not it is a power-law as they claim seems a bit weaker).

The network analyses in the paper are a bit odd, though they find many classic graph structures, such as a heavy-tailed link distribution and high amounts of clustering. Gratifyingly, the networks constructed from amateur and professional games are distinct, though in somewhat subtle ways.

Read the article here, or the short version on Wired!