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Community detection : computational complexity and approximation

Abstract : This thesis deals with community detection in the context of social networks. A social network can be modeled by a graph in which vertices represent members, and edges represent relationships. In particular, I study four different definitions of a community. First, a community structure can be defined as a partition of the vertices such that each vertex has a greater proportion of neighbors in its part than in any other part. This definition can be adapted in order to study only one community. Then, a community can be viewed as a subgraph in which every two vertices are at distance 2 in this subgraph. Finally, in the context of online meetup services, I investigate a definition for potential communities in which members do not know each other but are related by their common neighbors. In regard to these proposed definitions, I study computational complexity and approximation within problems that either relate to the existence of such communities or to finding them in graphs.
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Submitted on : Thursday, June 28, 2018 - 4:35:06 PM
Last modification on : Wednesday, October 14, 2020 - 4:01:17 AM
Long-term archiving on: : Thursday, September 27, 2018 - 12:25:41 PM


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  • HAL Id : tel-01825871, version 1


Thomas Pontoizeau. Community detection : computational complexity and approximation. Other [cs.OH]. Université Paris sciences et lettres, 2018. English. ⟨NNT : 2018PSLED007⟩. ⟨tel-01825871⟩



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