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Consensus clustering in complex networks

Consensus clustering in complex networks | Social Network Analysis #sna | Scoop.it

The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods.


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Social Network Analysis #sna
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Do online social media cut through the constraints that limit the size of offline social networks? by R. I. M. Dunbar

Do online social media cut through the constraints that limit the size of offline social networks? by R. I. M. Dunbar | Social Network Analysis #sna | Scoop.it
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Network analysis of hazard interconnections – Aaron Clark-Ginsberg

Network analysis of hazard interconnections – Aaron Clark-Ginsberg | Social Network Analysis #sna | Scoop.it
This post is the first in a series focused on using network analysis to analyse disaster risk. Disasters beget disasters. The 2010 Haiti earthquake for...
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How does misinformation spread online?

How does misinformation spread online? | Social Network Analysis #sna | Scoop.it
Recent studies that focus on misinformation online pointed out that the selective exposure to specific contents lead to ‘echo-chambers’ in which users tend to shape and reinforce their beliefs.
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Detecting automation of twitter accounts are you a human, bot, or cyborg

Twitter is a new web application playing dual roles of online social networking and microblogging. Users communicate with each other by publishing text-based p…
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Networks of military alliances, wars, and international trade

The incidence of interstate wars has dropped dramatically over time: The number of wars per pair of countries per year from 1950 to 2000 was roughly a 10th as high as it was from 1820 to 1949. This significant decrease in the frequency of wars correlates with a substantial increase in the number of military alliances per country and the stability of those alliances. We show that one possible explanation of this is an accompanying expansion of international trade. Increased trade decreases countries’ incentives to attack each other and increases their incentives to defend each other, leading to a stable and peaceful network of military and trade alliances that is consistent with observed data.

Networks of military alliances, wars, and international trade Matthew O. Jackson and Stephen Nei

PNAS 112(50):15277–15284

http://dx.doi.org/10.1073/pnas.1520970112 ;


Via Complexity Digest
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Based on the model we also examine some specific relationships, finding that countries with high levels of trade with their allies are less likely to be involved in wars with any other countries (including allies and nonallies), and that an increase in trade between two countries correlates with a lower chance that they will go to war with each other.
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Meritocracy and Topocracy of Networks - Cesar Hidalgo - YouTube

https://youtu.be/CTJ8TAMv3sk
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Postcapitalism lecture: London School of Economics

Postcapitalism lecture: London School of Economics | Social Network Analysis #sna | Scoop.it
In 1991 the nobel prizewinner Herbert Simon conducted a thought experiment: what would the economy look like to Martians?
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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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WEF16 Davos Twitter Performance Analysis

WEF16 Davos Twitter Performance Analysis | Social Network Analysis #sna | Scoop.it

During the World Economic Forum in Davos 2016 (WEF16) we collected over 480 thousand Tweets with content relating to #wef

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How a small data design company visualized the world's scientific collaborations - Storybench

How a small data design company visualized the world's scientific collaborations - Storybench | Social Network Analysis #sna | Scoop.it
Cuban scientists tend to partner up with Germans, but so do French researchers. The Kenyans work with South Africans. But, unsurprisingly, the global all-stars of scientific collaboration are the United States and China. An interactive map recently published by Nature revealed this web of collaborations to visualize the entire globe’s scientific partnerships. Made up of a constellation of colorful dots superimposed over [...]
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Concurrent Bursty Behavior of Social Sensors in Sporting Events

Concurrent Bursty Behavior of Social Sensors in Sporting Events | Social Network Analysis #sna | Scoop.it
“The advent of social media expands our ability to transmit information and connect with others instantly, which enables us to behave as “social sensors.” Here, we studied concurrent bursty behavior of Twitter users during major sporting events to determine their function as social sensors. We show that the degree of concurrent bursts in tweets (posts) and retweets (re-posts) works as a strong indicator of winning or losing a game. More specifically, our simple tweet analysis of Japanese professional baseball games in 2013 revealed that social sensors can immediately react to positive and negative events through bursts of tweets, but that positive events are more likely to induce a subsequent burst of retweets. We confirm that these findings also hold true for tweets related to Major League Baseball games in 2015. Furthermore, we demonstrate active interactions among social sensors by constructing retweet networks during a baseball game. The resulting networks commonly exhibited user clusters depending on the baseball team, with a scale-free connectedness that is indicative of a substantial difference in user popularity as an information source. While previous studies have mainly focused on bursts of tweets as a simple indicator of a real-world event, the temporal correlation between tweets and retweets implies unique aspects of social sensors, offering new insights into human behavior in a highly connected world.”

Takeichi Y, Sasahara K, Suzuki R, Arita T (2015) Concurrent Bursty Behavior of Social Sensors in Sporting Events. PLoS ONE 10(12): e0144646. http://dx.doi.org/10.1371/journal.pone.0144646


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Network theory sheds new light on origins of consciousness

Network theory sheds new light on origins of consciousness | Social Network Analysis #sna | Scoop.it
Vanderbilt University researchers took a significant step toward answering longstanding questions about the origins of consciousness with a recent discovery of global changes in how brain areas communicate with one another during awareness.
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“We know there are numerous brain networks that control distinct cognitive functions such as attention, language and control, with each node of a network densely interconnected with other nodes of the same network, but not with other networks,” Marois said. “Consciousness appears to break down the modularity of these networks, as we observed a broad increase in functional connectivity between these networks with awareness.”

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Modeling financial networks based on interpersonal trust | Open Agent Based Modeling Consortium

Modeling financial networks based on interpersonal trust | Open Agent Based Modeling Consortium | Social Network Analysis #sna | Scoop.it
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Welcome to the Modeling Commons! -- NetLogo Modeling Commons #networks, #complexity

Welcome to the Modeling Commons! -- NetLogo Modeling Commons #networks, #complexity | Social Network Analysis #sna | Scoop.it
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Unbiased metrics of friends’ influence in multi-level networks

The spreading of information is of crucial importance for the modern information society. While we still receive information from mass media and other non-personalized sources, online social networks and influence of friends have become important personalized sources of information. This calls for metrics to measure the influence of users on the behavior of their friends. We demonstrate that the currently existing metrics of friends’ influence are biased by the presence of highly popular items in the data, and as a result can lead to an illusion of friends influence where there is none. We correct for this bias and develop three metrics that allow to distinguish the influence of friends from the effects of item popularity, and apply the metrics on real datasets. We use a simple network model based on the influence of friends and preferential attachment to illustrate the performance of our metrics at different levels of friends’ influence.

 

Unbiased metrics of friends’ influence in multi-level networks
Alexandre Vidmer, Matúš Medo and Yi-Cheng Zhang

EPJ Data Science 2015, 4:20  http://dx.doi.org/10.1140/epjds/s13688-015-0057-x ;


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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


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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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