YASCA: A collective intelligence approach for community detection in complex networks

Complex networks are frequently used for modeling interactions in real-world systems in diverse areas, such as sociology, biology, information spreading and exchanging, scientometrics and many other different areas. One key topological feature of real-world complex networks is that nodes are arranged in tightly knit groups that are loosely connected one to each other. Such groups are called communities. Nodes composing a community are generally admitted to share common proprieties and/or be involved in a same function and/or having a same role. Hence, unfolding the community structure of a network could give us much insights about the overall structure a complex network. We distinguish between two different problems: partitioning the whole graph into (eventually overleaping) communities [Fortunato 2010] and identifying ego-centered communities for a given query node [Kanawati 2014a]. In this work we propose a new algorithm, YASCA, that use local community identification in order to compute a global graph partition into communities. The algorithm belongs to the seed centric algorithms family [Kanawati 2014b]. The basic idea of seed centric algorithms is to select a set of nodes (i.e. seeds) around which communities are constructed. Being based on local computations, these approaches are very attractive to deal with large-scale and/or dynamic networks. Different algorithms apply different policies for seed selection and for community construction around seeds. The number of seed nodes can be pre-determined [Khorasgani et al. 2010] or computed by the approach itself [Kanawati 2011]. The seed selection process can be : random [Khorasgani et al. 2010] or informed [Kanawati 2011]. The community construction can be made applying consensus techniques, expansion techniques or agglomeration techniques [Kanawati 2011]. Next we propose an original seed centric approach that apply an ensemble clustering approach [Strehl and Ghosh 2003] to different network partitions derived from ego-centered communities computed for each selected seed.