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In order to find a cover which allows nodes to be shared among several communities, we propose a simple fuzzy community detection algorithm, which is based on an existing partition detection technique. For the performance of overlapping nodes that makes the partition ambiguous, a new extended modularity is introduced to qualify covers. With modularity(More)
  • Qinna Wang
  • 2014 International Conference on Data Science and…
  • 2014
We tackle the problem of predicting future links in dynamic networks. For this, we work with the Debian Mailing Lists. In this dataset, a user can post a question to the debian list and other users can reply it by email forming a thread. We show that the number of threads shared in the past between users is a better feature to predict future email exchanges(More)
Time evolution is one important feature of communities in network science. It is related with capturing critical events, characterizing community members, and predicting behaviours of communities in networks with time varying. However, most of existing community detection techniques are proposed for static networks. Here, we present a new framework to(More)
Twitter is a social microbloging system which has become one of the most important ways for sharing information online. The basic principles of Twitter are (i) a user can follow other users in order to see the short messages (tweets) these users are posting on their profile, (ii) the user can also retweet these messages in order to make them available to(More)
A link stream is a collection of triplets (t,u,v) indicating that an interaction occurred between u and v at time t. Link streams model many realworld situations like email exchanges between individuals, connections between devices, and others. Much work is currently devoted to the generalization of classical graph and network concepts to link streams. In(More)
In this paper, we investigate the role of mentions on tweet propagation. We propose a novel tweet propagation model SIR_MF based on a multiplex network framework, that allows to analyze the effects of mentioning on final retweet count. The basic bricks of this model are supported by a comprehensive study of multiple real datasets and simulations of the(More)