In this session we will work through the whole process of social network analysis: from downloading connections using Twitter REST-based API, to implementing our own PageRank algorithm which finds the most central Twitter accounts. In the process you’ll see how we can use F# type providers to access data and harness the power of the statistical language R to run some machine learning algorithms.
At the end, you’ll know how to run your own analysis on data from Twitter and how to use data science tools to gain insights from social networks.
There was an amazing array of images of financial networks at the seminar on Financial Risk and Network Theory to launch the new journal on Network Theory in Finance. Have a look at these visualizations from some of the 23 presentations.
This catalog is a complement to “Creating More Effective Graphs” by Naomi Robbins. All graphs were produced using the Rlanguage and the add-on packageggplot2, written by Hadley Wickham. The gallery is maintained by Joanna Zhao andJennifer Bryan.
It’s taken us 4 years to scale Web Summit from 400 attendees to 20,000 and a bunch of physicists have played a big part. Back in 2010, 3 international journalists showed up, this year it will exceed 1,200. Investors...
Today I discovered Telenovelas. Telenovelas are short limited run programs similar to soap opera, they are popular in Spanish language counties and they are serious business. I stumbled across a clip on youtube and was instantly hooked. Check this out:…Read more ›
If you want to map cultural hubs throughout time, you can track where history's most notable figures—like Leonardo da Vinci, Jane Austen, and Steve Jobs—were born and died. That was the thinking of Dr. Maximilian Schich, associate professor for art and technology at the University of Texas at Dallas. Schich and his team took data on more than 100,000 notable...
Description of the book Social and Economic Networks by Jackson, M.O., published by Princeton University Press
Networks of relationships help determine the careers that people choose, the jobs they obtain, the products they buy, and how they vote. The many aspects of our lives that are governed by social networks make it critical to understand how they impact behavior, which network structures are likely to emerge in a society, and why we organize ourselves as we do. In Social and Economic Networks, Matthew Jackson offers a comprehensive introduction to social and economic networks, drawing on the latest findings in economics, sociology, computer science, physics, and mathematics. He provides empirical background on networks and the regularities that they exhibit, and discusses random graph-based models and strategic models of network formation. He helps readers to understand behavior in networked societies, with a detailed analysis of learning and diffusion in networks, decision making by individuals who are influenced by their social neighbors, game theory and markets on networks, and a host of related subjects. Jackson also describes the varied statistical and modeling techniques used to analyze social networks. Each chapter includes exercises to aid students in their analysis of how networks function.
When it comes to machine learning, building quality training data can be the toughest part of your work. Our goal was to train a SVM classifier that would guess the demographics of a given twitter user based on the content of their tweets, namely their age, gender, education(low/mid/high) and location (city/county). For that we needed a data set containing profiles and their demographics. To simplify the problem, we decided to limit ourselves to users located in the UK, and to tweets written in English.
Based on a proprietary analysis of over 2000 startup companies in mainly Germany and beyond (predominantly Europe) and their ties to over 500 relevant investors from across the world (angel investors, VC funds across all stages, ranging from the West Coast in the US to Hong Kong) which have invested in those startups, we highlight the importance of social network analysis (SNA). The dynamic network screening, the social capital related insights as well as ongoing portfolio as well as stakeholder monitoring represent just a fraction of possibilities when it comes to social network analysis` relevance for investing in the startup ecosystem. Whether you invest directly, search for co-investors as an investor or even funding as a startup, social network analytic insights will become mainstream in decision making complementing existing financial data in the future.
Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.