Nouveaux algorithmes pour la détection de communautés disjointes et chevauchantes basés sur la propagation de labels et adaptés aux grands graphes.

Abstract : Graphs are mathematical structures amounting to a set of nodes (objects or persons) in which some pairs are in linked with edges. Graphs can be used to model complex systems.One of the main problems in graph theory is the community detection problemwhich aims to find a partition of nodes in the graph to understand its structure.For instance, by representing insurance contracts by nodes and their relationship by edges,detecting groups of nodes highly connected leads to detect similar profiles and to evaluate risk profiles. Several algorithms are used as aresponse to this currently open research field.One of the fastest method is the label propagation.It's a local method, in which each node changes its own label according toits neighbourhood.Unfortunately, this method has two major drawbacks. The first is the instability of the method. Each trialgives rarely the same result.The second is a bad propagation which can lead to huge communities without sense (giant communities problem).The first contribution of the thesis is i) proposing a stabilisation methodfor the label propagation with artificial dams on edges of some networks in order to limit bad label propagations. Complex networks are also characterized by some nodes which may belong to several communities,we call this a cover.For example, in Protein–protein interaction networks, some proteins may have several functions.Detecting these functions according to their communities could help to cure cancers. The second contribution of this thesis deals with the ii)implementation of an algorithmwith functions to detect potential overlapping nodes .The size of the graphs is also to be considered because some networks contain several millions of nodes and edges like the Amazon product co-purchasing network.We propose iii) a parallel and a distributed version of the community detection using core label propagation.A study and a comparative analysis of the proposed algorithms will be done based on the quality of the resulted partitions and covers.
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Submitted on : Wednesday, June 7, 2017 - 4:12:41 PM
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Jean-Philippe Attal. Nouveaux algorithmes pour la détection de communautés disjointes et chevauchantes basés sur la propagation de labels et adaptés aux grands graphes.. Informatique [cs]. Université de Cergy Pontoise, 2017. Français. ⟨NNT : 2017CERG0842⟩. ⟨tel-01534480⟩

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