Social networks pervade our everyday lives: we interact, influence, and are influenced by our friends and acquaintances. With the advent of the World Wide Web, large amounts of data on social networks have become available, allowing the quantitative analysis of the distribution of information on them, including behavioral traits and fads. Recent studies of correlations among members of a social network, who exhibit the same trait, have shown that individuals influence not only their direct contacts but also friends’ friends, up to a network distance extending beyond their closest peers. Here, we show how such patterns of correlations between peers emerge in networked populations. We use standard models (yet reflecting intrinsically different mechanisms) of information spreading to argue that empirically observed patterns of correlation among peers emerge naturally from a wide range of dynamics, being essentially independent of the type of information, on how it spreads, and even on the class of underlying network that interconnects individuals. Finally, we show that the sparser and clustered the network, the more far reaching the influence of each individual will be. DOI: http://dx.doi.org/10.1103/PhysRevLett.112.098702
Origin of Peer Influence in Social Networks Phys. Rev. Lett. 112, 098702 – Published 6 March 2014 Flávio L. Pinheiro, Marta D. Santos, Francisco C. Santos, and Jorge M. Pacheco
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…
It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention, and explicit social feedback to predict the temporal dynamics of user behavior.
The combination of the network theoretic approach with recently available abundant economic data leads to the development of novel analytic and computational tools for modelling and forecasting key economic indicators. The main idea is to introduce a topological component into the analysis, taking into account consistently all higher-order interactions. We present three basic methodologies to demonstrate different approaches to harness the resulting network gain. First, a multiple linear regression optimisation algorithm is used to generate a relational network between individual components of national balance of payment accounts. This model describes annual statistics with a high accuracy and delivers good forecasts for the majority of indicators. Second, an early-warning mechanism for global financial crises is presented, which combines network measures with standard economic indicators. From the analysis of the cross-border portfolio investment network of long-term debt securities, the proliferation of a wide range of over-the-counter-traded financial derivative products, such as credit default swaps, can be described in terms of gross-market values and notional outstanding amounts, which are associated with increased levels of market interdependence and systemic risk. Third, considering the flow-network of goods traded between G-20 economies, network statistics provide better proxies for key economic measures than conventional indicators. For example, it is shown that a country's gate-keeping potential, as a measure for local power, projects its annual change of GDP generally far better than the volume of its imports or exports.
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.
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