Identification de r\^oles communautaires dans des r\'eseaux orient\'es appliqu\'ee \`a Twitter
Nicolas Dugu\'e (LIFO), Vincent Labatut, Anthony Perez (LIFO)

TL;DR
This paper extends community role identification methods to directed networks and applies it to Twitter, revealing specific roles of users like social capitalists through an unsupervised approach.
Contribution
It generalizes existing community role measures to directed networks and introduces an unsupervised method for role determination, demonstrated on Twitter data.
Findings
Identified specific roles of social capitalists in Twitter.
Extended community role measures to directed networks.
Demonstrated effectiveness of the method on real social media data.
Abstract
The notion of community structure is particularly useful when analyzing complex networks, because it provides an intermediate level, compared to the more classic global (whole network) and local (node neighborhood) approaches. The concept of community role of a node was derived from this base, in order to describe the position of a node in a network depending on its connectivity at the community level. However, the existing approaches are restricted to undirected networks, use topological measures which do not consider all aspects of community-related connectivity, and their role identification methods are not generalizable to all networks. We tackle these limitations by generalizing and extending the measures, and using an unsupervised approach to determine the roles. We then illustrate the applicability of our method by analyzing a Twitter network.We show how our modifications allow…
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Taxonomy
TopicsSocial Capital and Networks · Complex Network Analysis Techniques · Peer-to-Peer Network Technologies
