Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of information through one-mode projections, and lack of interpretability. Here we solve the community detection problem for bipartite networks by formulating a bipartite stochastic block model, which explicitly includes vertex type information and may be trivially extended to $k$-partite networks. This bipartite stochastic block model yields a projection-free and statistically principled method for community detection that makes clear assumptions and parameter choices and yields interpretable results. We demonstrate this model's ability to efficiently and accurately find community structure in synthetic bipartite networks with known structure and in real-world bipartite networks with unknown structure, and we characterize its performance in practical contexts.
FIFA World Cup 2014, the biggest sporting event in four years (sorry Olympics) is starting today. The tournament holds 736 players from 32 countries. When the players are not playing for their national teams, they play in 301 different clubs. Players from different national teams meet in these clubs. For example, Manchester United has players from 9 different national teams. This means that players in the World Cup who play in Manchester United know players from at least eight different national teams. Why is this important? If two players belong to the same team (national or club), they have a social connection. Using
In this mini lecture, Véronique Van Vlasselaer talks about how social networks can be leveraged to uncover fraud. Véronique is working in the DataMiningApps group led by Prof. dr. Bart Baesens at the KU Leuven (University of Leuven), Belgium.
Learn and talk about Social network analysis, and check out Social network analysis on Wikipedia, Youtube, Google News, Google Books, and Twitter on Digplanet. Digplanet gathers together information and people from all over the Internet, all focused on Social network analysis, and makes it easy to learn, explore, and join the Digparty and talk to real people who are also interested in Social network analysis.
This course of 25 lectures, filmed at Cornell University in Spring 2014, is intended for newcomers to nonlinear dynamics and chaos. It closely follows Prof. Strogatz's book, "Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering." The mathematical treatment is friendly and informal, but still careful. Analytical methods, concrete examples, and geometric intuition are stressed. The theory is developed systematically, starting with first-order differential equations and their bifurcations, followed by phase plane analysis, limit cycles and their bifurcations, and culminating with the Lorenz equations, chaos, iterated maps, period doubling, renormalization, fractals, and strange attractors. A unique feature of the course is its emphasis on applications. These include airplane wing vibrations, biological rhythms, insect outbreaks, chemical oscillators, chaotic waterwheels, and even a technique for using chaos to send secret messages. In each case, the scientific background is explained at an elementary level and closely integrated with the mathematical theory. The theoretical work is enlivened by frequent use of computer graphics, simulations, and videotaped demonstrations of nonlinear phenomena. The essential prerequisite is single-variable calculus, including curve sketching, Taylor series, and separable differential equations. In a few places, multivariable calculus (partial derivatives, Jacobian matrix, divergence theorem) and linear algebra (eigenvalues and eigenvectors) are used. Fourier analysis is not assumed, and is developed where needed. Introductory physics is used throughout. Other scientific prerequisites would depend on the applications considered, but in all cases, a first course should be adequate preparation
Nonlinear Dynamics and Chaos - Steven Strogatz, Cornell University
Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.
""As knowledge-intensive social events, conferences open up a space in which people and organizations can share and generate knowledge, intensify their existing cooperation activities and establish new contacts. By bringing together people
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Recent research has focused on the monitoring of global–scale online data for improved detection of epidemics, mood patterns, movements in the stock market political revolutions, box-office revenues, consumer behaviour and many other important phenomena. However, privacy considerations and the sheer scale of data available online are quickly making global monitoring infeasible, and existing methods do not take full advantage of local network structure to identify key nodes for monitoring. Here, we develop a model of the contagious spread of information in a global-scale, publicly-articulated social network and show that a simple method can yield not just early detection, but advance warning of contagious outbreaks. In this method, we randomly choose a small fraction of nodes in the network and then we randomly choose a friend of each node to include in a group for local monitoring. Using six months of data from most of the full Twittersphere, we show that this friend group is more central in the network and it helps us to detect viral outbreaks of the use of novel hashtags about 7 days earlier than we could with an equal-sized randomly chosen group. Moreover, the method actually works better than expected due to network structure alone because highly central actors are both more active and exhibit increased diversity in the information they transmit to others. These results suggest that local monitoring is not just more efficient, but also more effective, and it may be applied to monitor contagious processes in global–scale networks.
The Bechdel test is a popular tool to analyze the role of women in movies, defining three conditions for a movie to pass the test: It contains two female characters Who talk to each other About something besides a man…
The most popular accounts on twitter have millions of followers, but what are their demographics like? Twitter doesn’t collect or release this kind of information, and even things like name and location are only voluntarily added to people’s profiles. Unlike Google+ … Continue reading →
The temporal evolution of the network yields to another perspective of social structure and, in some cases, aggregating the data in a time window might blur out important temporal structures on information diffusion, community or opinion formation, etc. Although many of the commercial and free Social Network Analysis software have tools to visualize static networks, there are no so many options out there for dynamical networks.
In this post I will show you how to render the network at each time step and how to encode all snapshots into a video file using the igraph package in R and ffmpeg. The idea is very simple
generate a number of snapshots of the network at different times using R and igraph, andthen put them together in a video file using ffmpeg.
Proxy Networks--Analyzing One Network To Reveal Another
Two books are linked if they were bought together at a major retailer on the web. I call these "buddy books". A link was drawn if either book of a pair listed the other as a buddy. The data made public by the retailer shows just the "best buddies" — the strongest ties. Other patterns may emerge with investigation of weaker ties. Amazon reveals only the top five or six books bought concurrently with a particular book. Seeing dozens of buddy books for each book would reveal some of the weaker ties and no doubt affect the structure of our network.