Influence et contagion
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Influence et contagion
L'influence et la contagion dans la cyberculture
Curated by luiy
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The Evolution of Beliefs over Signed Social Networks | #SNA #influence

The Evolution of Beliefs over Signed Social Networks | #SNA #influence | Influence et contagion | Scoop.it
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We study the evolution of opinions (or beliefs) over a social network modeled as a signed graph. The sign attached to an edge in this graph characterizes whether the corresponding individuals or end nodes are friends (positive links) or enemies (negative links). Pairs of nodes are randomly selected to interact over time, and when two nodes interact, each of them updates its opinion based on the opinion of the other node and the sign of the corresponding link. This model generalizes DeGroot model to account for negative links: when two enemies interact, their opinions go in opposite directions. We provide conditions for convergence and divergence in expectation, in mean-square, and in almost sure sense, and exhibit phase transition phenomena for these notions of convergence depending on the parameters of the opinion update model and on the structure of the underlying graph. We establish a {\it no-survivor} theorem, stating that the difference in opinions of any two nodes diverges whenever opinions in the network diverge as a whole. We also prove a {\it live-or-die} lemma, indicating that almost surely, the opinions either converge to an agreement or diverge. Finally, we extend our analysis to cases where opinions have hard lower and upper limits. In these cases, we study when and how opinions may become asymptotically clustered to the belief boundaries, and highlight the crucial influence of (strong or weak) structural balance of the underlying network on this clustering phenomenon.

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graph-tool: Efficent network analysis with #python | #SNA #tools

graph-tool: Efficent network analysis with #python | #SNA #tools | Influence et contagion | Scoop.it
graph-tool: Efficent network analysis with python
luiy's insight:

An extensive array of features is included, such as support for arbitrary vertex, edge or graph properties, efficient "on the fly" filtering of vertices and edges, powerful graph I/O using the GraphML, GML and dot file formats, graph pickling, graph statistics (degree/property histogram, vertex correlations, average shortest distance, etc.), centrality measures, standard topological algorithms (isomorphism, minimum spanning tree, connected components, dominator tree, maximum flow, etc.), generation of random graphs with arbitrary degrees and correlations, detection of modules and communities via statistical inference ,,,,,, 

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Rescooped by luiy from Social Network Analysis #sna
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Proxy #Networks - Analyzing One Network To Reveal Another | #SNA #political

Proxy #Networks - Analyzing One Network To Reveal Another | #SNA #political | Influence et contagion | Scoop.it
Proxy Networks--Analyzing One Network To Reveal Another

Via ukituki
luiy's insight:

This article uses this network tie information to construct social networks of "buddy books". A lthough the actual political affiliation of each book purchaser is not known, the structure of the buddy book network shows that there are two clearly divided groups: a larger and morediffuse left-of-center readership, and a smaller and more closely tied right-of-centerreadership. Types or networks of readers linked to a specific author are also studied.

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ukituki's curator insight, April 15, 2014 5:56 PM

Two books are linked if they were bought together at a major retailer on the web. I call these "buddy books". A link was drawn if either book of a pair listed the other as a buddy. The data made public by the retailer shows just the "best buddies" — the strongest ties. Other patterns may emerge with investigation of weaker ties. Amazon reveals only the top five or six books bought concurrently with a particular book. Seeing dozens of buddy books for each book would reveal some of the weaker ties and no doubt affect the structure of our network.

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“Follow the Leader”: A #Centrality Guided #Clustering and Its Application to #SNA

“Follow the Leader”: A #Centrality Guided #Clustering and Its Application to #SNA | Influence et contagion | Scoop.it

"Within graph theory and network analysis, centrality of a vertex measures the relative importance of a vertex within a graph. The centrality plays key role in network analysis and has been widely studied using different methods. Inspired by the idea of vertex centrality, a novel centrality guided clustering (CGC) is proposed in this paper. Different from traditional clustering methods which usually choose the initial center of a cluster randomly, the CGC clustering algorithm starts from a “LEADER”—a vertex with the highest centrality score—and a new “member” is added into the same cluster as the “LEADER” when some criterion is satisfied. The CGC algorithm also supports overlapping membership. Experiments on three benchmark social network data sets are presented and the results indicate that the proposed CGC algorithm works well in social network clustering."


Via João Greno Brogueira
luiy's insight:

In this work, a novel hierarchical clustering algorithm is proposed for social network clustering. Traditional clustering methods, such as -means, usually choose clustering centers randomly, and the hierarchical clustering algorithms usually start from two elements with shortest distance. Different from these methods, this work chooses the vertex with highest centrality score as the starting point. If one does some analysis on social network datasets, one may notice that in each community, there is usually some member (or leader) who plays a key role in that community. In fact, centrality is an important concept [13] within social network analysis. High centrality scores identify members with the greatest structural importance in a network and these members are expected to play key roles in the network. Based on this observation, this work proposes to start clustering from the member with highest centrality score. That is, a group is formed starting from its “leader,” and a new “member” is added into an existing group based on its total relation with the group. The main procedure is as follows. Choose the vertex with the highest centrality score which is not included in any existing group yet and call this vertex a “LEADER.” A new group is created with this “LEADER.” Repeatedly add one vertex to an existing group if the following criterion is satisfied: the density of the newly extended group is above a given threshold.

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#Clustering #memes in social media streams | #algorithms #sna

#Clustering #memes in social media streams | #algorithms #sna | Influence et contagion | Scoop.it
luiy's insight:

The problem of clustering content in social media has pervasive applications, including the identification of discussion topics, event detection, and content recommendation. Here we describe a streaming framework for online detection and clustering of memes in social media, specifically Twitter.

 

A pre-clustering procedure, namely protomeme detection, first isolates atomic tokens of information carried by the tweets. Protomemes are thereafter aggregated, based on multiple similarity measures, to obtain memes as cohesive groups of tweets reflecting actual concepts or topics of discussion.

 

The clustering algorithm takes into account various dimensions of the data and metadata, including natural language, the social network, and the patterns of information diffusion. As a result, our system can build clusters of semantically, structurally, and topically related tweets.


The clustering process is based on a variant of Online K-means that incorporates a memory mechanism, used to "forget" old memes and replace them over time with the new ones. The evaluation of our framework is carried out by using a dataset of Twitter trending topics.


Over a one-week period, we systematically determined whether our algorithm was able to recover the trending hashtags. We show that the proposed method outperforms baseline algorithms that only use content features, as well as a state-of-the-art event detection method that assumes full knowledge of the underlying follower network. We finally show that our online learning framework is flexible, due to its independence of the adopted clustering algorithm, and best suited to work in a streaming scenario.

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Rescooped by luiy from Complex Systems and X-Events
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PLOS ONE #Complex systems articles | #ABM #netwoks #research

PLOS ONE #Complex systems articles | #ABM #netwoks #research | Influence et contagion | Scoop.it

PLOS ONE: an inclusive, peer-reviewed, open-access resource from the PUBLIC LIBRARY OF SCIENCE. Reports of well-performed scientific studies from all disciplines freely available to the whole world.


Via Bryan A. Knowles, Bernard Ryefield, Luciana Viter, Roger D. Jones, PhD
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