In this paper, we propose methods that identify influential edges in a network. The paper uses the proposed methods to analyse two networks from disparate applications—win–loss data of teams competing in the 2011 season of NCAA Football Bowl Subdivision and the 2001 Enron employee email dataset. Several edge measures are proposed. The first set of measures adapt node measures to the context of analysing the importance of edges. The second set include measuring betweenness, rank sensitivity to perturbations in the network and the sensitivity of the ranking as measured by a linear programme formulation. The methods are applied to the aforementioned networks and the benefits and appropriateness of the methods are discussed and contrasted.
The financial crisis clearly illustrated the importance of characterizing the level of ‘systemic’ risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998–2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear – but unpredictable – signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies.
Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks.
Note: This post is co-written with Piotr Sapieżyński Is it possible for a small computer science course to exert measurable influence (trending topics) on Twitter..
Is it possible for a small computer science course to exert measurable influence (trending topics) on Twitter, a massive social network with hundreds of millions of users? The surprising answer to that question is “yes”. That’s exactly what we did this year, using simple Python scripts and the Twitter API. Below we explain why & how + some of our findings along the way.
This graduate-level course will examine modern techniques for analyzing and modeling the structure and dynamics of complex networks. The focus will be on statistical algorithms and methods, and both lectures and assignments will emphasize model interpretability and understanding the processes that generate real data. Applications will be drawn from computational biology and computational social science. No biological or social science training is required. (Note: this is not a scientific computing course, but there will be plenty of computing for science.)
Barabasi argues that, rather than being random, humans actually act in predictable patterns. We go along for long periods of quiet routine followed suddenly by loud bursts of activity. Barabasi demonstrates that these breaks in routine, or "bursts," are present in all aspects of our existence— in the way we write emails, spend our money, manage our health, form ideas. Barabasi has even found "burstiness" in our webpage clicking activity and the online news cycle."
This is a guest post by Dr. Ralph Ohr. Dr. Ohr has extensive experience in product/innovation management for international technology-based companies. His particular interest is targeted at the intersection of organizational and human innovation capabilities.
Everyone wishes their content goes viral, like this baby Halloween costume, who got almost 4 million views in two days: But this doesn’t always have to be the case.
What is Perceived Virality?
Perceived virality is rather simple – it’s the sense of content appearing to go viral, but within a targeted or segmented network. Because a lot of people within that specific network are exposed to the content, to them, it appears as “going viral,” even if only a few hundred or few thousand people have seen/shared the content.
How Does Perceived Virality Work?
This is where it gets interesting – perceived virality works because of the overlapping relationships within a network, and the density of these relationships within a network.
One of the main criticisms of Coase’s work is also being tossed out the window. “So, a key criticism is that the [Coase] theorem is almost always inapplicable in economic reality, because real-world transaction costs are rarely low enough to allow for efficient bargaining.” - Wikipedia. In the network era, real-world transaction costs diminish. Furthermore, transaction costs between networked individuals are getting to be less than transaction costs inside organizations. Workers today often have faster access to knowledge outside their enterprises. With knowledge work, this begs the question of why we need organizations for anything other than support.
Venezuelan economist Ricardo Hausmann and Chilean physicist César Hidalgo, in a joint effort of Harvard University and the Massachutes Institute of Technology MIT, draw a new world map of economic adventure, and suggest the Earth may not be flat.
Describing a social network based on a particular type of human social interaction, say, Facebook, is conceptually simple: a set of nodes representing the people involved in such a network, linked by their Facebook connections. But, what kind of network structure would one have if all modes of social interactions between the same people are taken into account and if one mode of interaction can influence another? Here, the notion of a “multiplex” network becomes necessary. Indeed, the scientific interest in multiplex networks has recently seen a surge. However, a fundamental scientific language that can be used consistently and broadly across the many disciplines that are involved in complex systems research was still missing. This absence is a major obstacle to further progress in this topical area of current interest. In this paper, we develop such a language, employing the concept of tensors that is widely used to describe a multitude of degrees of freedom associated with a single entity.
Our tensorial formalism provides a unified framework that makes it possible to describe both traditional “monoplex” (i.e., single-type links) and multiplex networks. Each type of interaction between the nodes is described by a single-layer network. The different modes of interaction are then described by different layers of networks. But, a node from one layer can be linked to another node in any other layer, leading to “cross talks” between the layers. High-dimensional tensors naturally capture such multidimensional patterns of connectivity. Having first developed a rigorous tensorial definition of such multilayer structures, we have also used it to generalize the many important diagnostic concepts previously known only to traditional monoplex networks, including degree centrality, clustering coefficients, and modularity.
We think that the conceptual simplicity and the fundamental rigor of our formalism will power the further development of our understanding of multiplex networks.
#SNA: Communities, roles, and informational organigrams in directed networks: the Twitter network of the UK riots. By Mariano Beguerisse-Díaz, Guillermo Garduño-Hernández, Borislav Vangelov, Sophia N....
Research shows that being the most connected person is not an effective way to build your network. The single best strategy is one that almost no one talks about.
As you can see in the chart, the further to the right you go toward a closed network, the more you’ll repeatedly hear the same ideas, which reaffirm what you already believe. The further left you go toward an open network, the more you’ll be exposed to new ideas. People to the left are significantly more successful than those to the right.
Public information about one’s coworkers, friends, family, and acquaintances, as well as one’s associations with them, implicitly reveals private information. Social networking Web sites, e–mail, instant messaging, telephone, and VoIP are all technologies steeped in network data — data relating one person to another. Network data shifts the locus of information control away from individuals, as the individual’s traditional and absolute discretion is replaced by that of his social network. Our research demonstrates a method for accurately predicting the sexual orientation of Facebook users by analyzing friendship associations. After analyzing 4,080 Facebook profiles from the MIT network, we determined that the percentage of a given user’s friends who self–identify as gay male is strongly correlated with the sexual orientation of that user, and we developed a logistic regression classifier with strong predictive power. Although we studied Facebook friendship ties, network data is pervasive in the broader context of computer–mediated communication, raising significant privacy issues for communication technologies to which there are no neat solutions.