What makes a meme— an idea, a phrase, an image—go viral? For starters, the meme must have broad appeal, so it can spread not just within communities of like-minded individuals but can leap from one community to the next. Researchers, by mining public Twitter data, have found that a meme's “virality” is often evident from the start. After only a few dozen tweets, a typical viral meme (as defined by tweets using a given hashtag) will already have caught on in numerous communities of Twitter users. In contrast, a meme destined to peter out will resonate in fewer groups.
The Libor manipulation scandal has ensnared at least 17 financial institutions and 22 individuals in a wide-ranging investigation spanning 11 countries and four continents. So far, it has netted at least $5 billion in penalties, with more on the way. Below, we've taken the most complete list of allegedly involved parties, compiled by WSJ reporters and editors, and mapped an extensive web of 298 reported connections that reveals the depth of the alleged conspiracy. Connections do not represent allegations of wrongdoing. The Journal has attempted to contact every institution and individual mentioned in this graphic. Their comments, if any, are included.
Within its limits, SNA can be applied to identify individuals or organizations within a network, generate new leads and simulate flows of information or money throughout a network.
Like every analytic technique, SNA has great utility for the right question. Within its limits, SNA is unmatched and can be usefully applied to identify key individuals or organizations within a network, generate new leads and simulate the flows of information or money throughout a network. SNA, however, remains just an answer, not the answer. Used inappropriately or without a full understanding of the limits of the method and analysts will only be finding new and more technically sophisticated ways to fail. That, then, is the primary job of the modern day analyst: making the judgment call of which techniques to use and when. Equally as important as knowing when to use SNA is knowing when not to use it.
Millions of geo tweets in various languages, discussing anything from 'hey, I'm here' to finance, geopolitics or marketing. How do you make sense of them?
We’ve used name recognition (applied onomastics) to filter information and produce unique maps of the e-Diasporas. Where are the digitally connected Italian, Turkish and Russian today? They may be migrants, tourists, business travellers, student, visiting scientists…
The global spread of epidemics, rumors, opinions, and innovations are complex, network-driven dynamic processes. The combined multiscale nature and intrinsic heterogeneity of the underlying networks make it difficult to develop an intuitive understanding of these processes, to distinguish relevant from peripheral factors, to predict their time course, and to locate their origin. However, we show that complex spatiotemporal patterns can be reduced to surprisingly simple, homogeneous wave propagation patterns, if conventional geographic distance is replaced by a probabilistically motivated effective distance. In the context of global, air-traffic–mediated epidemics, we show that effective distance reliably predicts disease arrival times. Even if epidemiological parameters are unknown, the method can still deliver relative arrival times. The approach can also identify the spatial origin of spreading processes and successfully be applied to data of the worldwide 2009 H1N1 influenza pandemic and 2003 SARS epidemic.
The Hidden Geometry of Complex, Network-Driven Contagion Phenomena Dirk Brockmann, Dirk Helbing
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.
The view aims to map out accounts that are followed by 10 or more people from a sample of about 200 or so followers of @onthewight. The network is layed out according to a force directed layout algorithm with a dash of aesthetic tweaking; nodes are coloured based on community grouping as identified using the Gephi modularity statistic. Which has it’s issues, but it’s a start. The nodes are sized in the first case according to PageRank.
Animal behavior isn't complicated, but it is complex. Nicolas Perony studies how individual animals -- be they Scottish Terriers, bats or meerkats -- follow simple rules that, collectively, create larger patterns of behavior. And how this complexity born of simplicity can help them adapt to new circumstances, as they arise.
Drawing from a combination of network analysis measurements, Erik Brynjolfsson and Shachar Reichman present methods from their research on predicting the future success of researchers.
We analyzed the combination of the publications network (i.e. citation network), the authors’ social network (i.e. co-authorship network) and the links that connect the 2 networks which generate a dual-network structure (see figure 1). Using data from Thomson-Reuters Web of Knowledge, we created a set of yearly snapshots of the papers-authors dual-networks from 1975 to 2012 on over 700,000 papers published in management, information systems and operations research journals. For each network snapshot we computed common centrality measures of it nodes as part of the variables in our models.
Since its first formulations almost a century ago, mathematical models for disease spreading contributed to understand, evaluate and control the epidemic processes.They promoted a dramatic change in how epidemiologists thought of the propagation of infectious diseases.In the last decade, when the traditional epidemiological models seemed to be exhausted, new types of models were developed.These new models incorporated concepts from graph theory to describe and model the underlying social structure.Many of these works merely produced a more detailed extension of the previous results, but some others triggered a completely new paradigm in the mathematical study of epidemic processes. In this review, we will introduce the basic concepts of epidemiology, epidemic modeling and networks, to finally provide a brief description of the most relevant results in the field.
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.