M. C. Nait-Hamoud
ABSTRACT
In this work, we contribute to solve the community detection problem by proposing an algorithm for the detection of disjoint communities' cores considering unweighted and undirected social graphs. The proposed algorithm is based on the removal of non-essential edges and induced isolated nodes from networks (graphs). To this purpose; we have built a model for predicting edges removal using Weighted Support Vector Machines (WSVM) trained on real-life social network datasets. The training phase was carried out by means of an appropriate proposed heuristic to label edges, and discriminating features extracted from both edges end-point nodes and network structure characteristics. Our designed algorithm shows promising results for communities' cores detection.
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