Social Network Analysis #sna
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Social Network Analysis #sna
Social Network Analysis
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Understanding Economic Complexity - Cesar Hidalgo - YouTube

Serious Science - http://serious-science.org/videos/289 The assistant professor at the MIT Media Lab and faculty associate at Harvard University's Center for...
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Prediction in complex systems: the case of the international trade network

Predicting the future evolution of complex systems is one of the main challenges in complexity science. Based on a current snapshot of a network, link prediction algorithms aim to predict its future evolution. We apply here link prediction algorithms to data on the international trade between countries. This data can be represented as a complex network where links connect countries with the products that they export. Link prediction techniques based on heat and mass diffusion processes are employed to obtain predictions for products exported in the future. These baseline predictions are improved using a recent metric of country fitness and product similarity. The overall best results are achieved with a newly developed metric of product similarity which takes advantage of causality in the network evolution.

Prediction in complex systems: the case of the international trade networkAlexandre Vidmer, An Zeng, Matúš Medo, Yi-Cheng Zhang

http://arxiv.org/abs/1511.05404


Via Complexity Digest
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[1509.08295] Detecting global bridges in networks

[1509.08295] Detecting global bridges in networks | Social Network Analysis #sna | Scoop.it
The identification of nodes occupying important positions in a network structure is crucial for the understanding of the associated real-world system. Usually, betweenness centrality is used to evaluate a node capacity to connect different graph regions. However, we argue here that this measure is not adapted for that task, as it gives equal weight to "local" centers (i.e. nodes of high degree central to a single region) and to "global" bridges, which connect different communities. This distinction is important as the roles of such nodes are different in terms of the local and global organisation of the network structure. In this paper we propose a decomposition of betweenness centrality into two terms, one highlighting the local contributions and the other the global ones. We call the latter bridgeness centrality and show that it is capable to specifically spot out global bridges. In addition, we introduce an effective algorithmic implementation of this measure and demonstrate its capability to identify global bridges in air transportation and scientific collaboration networks.

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Understanding Online Misinformation

Understanding Online Misinformation Alessandro Bessi November 5, 2015 - Pavia, Italy
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Mini Lecture: Social Network Analysis for Fraud Detection - YouTube

In this mini lecture, Véronique Van Vlasselaer talks about how social networks can be leveraged to uncover fraud. Véronique is working in the DataMiningApps ...
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A quick puzzle to tell whether you know what people are thinking

A quick puzzle to tell whether you know what people are thinking | Social Network Analysis #sna | Scoop.it
A mathematical quirk where the majority think they’re actually the minority.
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Online News Citation Network

Online News Citation Network | Social Network Analysis #sna | Scoop.it
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Network of citations between online news articles of major German and British news outlets, starting in June 2014. For the creation of the network, only those links between articles were extracted that are embedded in the articles' texts. The data set contains no content of the original articles, but URIs to each individual article are provided. Note that news outlets were added not at once but in steps. Therefore, there exists more data for some outlets and less for others. Most notably, British news outlets were only added in July 2015. The data set consists of three files:

 
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Dissecting the Big data Twitter Community through a Big data Lense #sna

Dissecting the Big data Twitter Community through a Big data Lense #sna | Social Network Analysis #sna | Scoop.it
BigData hashtag is hyperactive, with close to 2000 tweets each day with more than 20,000 tweeps. This post digs into the tweet archive from August 03-25 to understand dynamics about the Big data  
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#FollowWomenWednesday: Why It Succeeded — And Why It Won’t Last

#FollowWomenWednesday: Why It Succeeded — And Why It Won’t Last | Social Network Analysis #sna | Scoop.it
How long could #FollowWomenWednesday last? This was one of my questions in the first few weeks of this hashtag meme, which aimed to create more gender equality on Twitter.  
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Network Approaches for Interbank Markets

Invited talk on Network Approaches for Interbank Markets -research conference in Castellon, Spain.
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Analyzing Collaborative Governance Through Social Network Analysis: A Case Study of River Management Along the Waal River in The Netherlands #sna

Analyzing Collaborative Governance Through Social Network Analysis: A Case Study of River Management Along the Waal River in The Netherlands #sna | Social Network Analysis #sna | Scoop.it
Paperity: the 1st multidisciplinary aggregator of Open Access journals & papers. Free fulltext PDF articles from hundreds of disciplines, all in one place
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The Gremlin Graph Traversal Language

A presentation of Apache TinkerPop's Gremlin language with running examples over the MovieLens dataset. Presented August 19, 2015 at NoSQL NOW in San Jose, Cal…
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Understanding Human-Machine Networks: A Cross-Disciplinary Survey

In the current hyper-connected era, modern Information and Communication Technology systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such human-machine networks (HMNs) are embedded in the daily lives of people, both or personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, nor following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.

Understanding Human-Machine Networks: A Cross-Disciplinary SurveyMilena Tsvetkova, Taha Yasseri, Eric T. Meyer, J. Brian Pickering, Vegard Engen, Paul Walland, Marika Lüders, Asbjørn Følstad, George Bravos

http://arxiv.org/abs/1511.05324


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New algorithm cracks graph problem #sna #networks. Congratulations @barabasi, you push things forward

New algorithm cracks graph problem #sna #networks. Congratulations @barabasi, you push things forward | Social Network Analysis #sna | Scoop.it
A new algorithm efficiently solves the graph isomorphism problem, which has puzzled computer scientists for decades.
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Computers generally have little trouble determining if graphs are isomorphic. But for even the best algorithms, there is a worst-case scenario in which the solving time grows nearly exponentially as the number of nodes increases. Babai claims that he has developed an algorithm that evaluates even the trickiest graphs in what’s called quasipolynomial time, which computer scientists consider reasonable. “We weren’t even close to quasipolynomial,” Williams says. The solving time still increases along with the number of nodes, but it does so much more gradually.
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ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software

ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software | Social Network Analysis #sna | Scoop.it
Gephi is a network visualization software used in various disciplines (social network analysis, biology, genomics...).
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Who’s Doing What in Virtual Reality? A Network View on Technological Activity « Kenedict Innovation Analytics

Who’s Doing What in Virtual Reality? A Network View on Technological Activity « Kenedict Innovation Analytics | Social Network Analysis #sna | Scoop.it
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Where Web Content Meets True Network Intelligence

Where Web Content Meets True Network Intelligence | Social Network Analysis #sna | Scoop.it
An essay on the future of information networks and ‘socialized’ media…
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European Government Bond Correlation Dynamics: Taming Contagion Risks

In the timeframe from 2004-2015, the European government bond market experienced several different economic phases driven by the Euro convergence, but also by …
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Virality Prediction and Community Structure in Social Networks : Scientific Reports

Virality Prediction and Community Structure in Social Networks : Scientific Reports | Social Network Analysis #sna | Scoop.it
How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases.
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We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications.

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Automatically Discover Website Connections Through Tracking Codes

Automatically Discover Website Connections Through Tracking Codes | Social Network Analysis #sna | Scoop.it
This article originally appeared on the AutomatingOSINT.com blog. Fellow Bellingcat contributor Lawrence Alexander did some really interesting OSINT work on analyzing the hidden links between websi...
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Trend of Narratives in the Age of Misinformation

Trend of Narratives in the Age of Misinformation | Social Network Analysis #sna | Scoop.it
 Social media enabled a direct path from producer to consumer of contents changing the way users get informed, debate, and shape their worldviews. Such a disintermediation might weaken consensus on social relevant issues in favor of rumors, mistrust, or conspiracy thinking—e.g., chem-trails inducing global warming, the link between vaccines and autism, or the New World Order conspiracy. Previous studies pointed out that consumers of conspiracy-like content are likely to aggregate in homophile
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