Authors:
Imen Ben El Kouni
1
;
2
;
Wafa Karoui
2
;
3
and
Lotfi Ben Romdhane
1
;
2
Affiliations:
1
Université de Sousse, ISITCom, 4011, Sousse, Tunisia
;
2
Université de Sousse, Laboratoire MARS LR17ES05, ISITCom, 4011, Sousse, Tunisia
;
3
Université de Tunis El Manar, Institut Superieur d’Informatique, 2080, Tunis, Tunisia
Keyword(s):
Attributed Networks, Overlapping Community Detection, Node Similarity, Weighted Graph.
Abstract:
Several networks are enriched by two types of information: the network topology and the attributes information about each node. Such graphs are typically called attributed networks, where the attributes are always as important as the topological structure. In these attributed networks, community detection is a critical task that aims to discover groups of similar users. However, the majority of the existing community detection methods in attributed networks were created to identify separated groups in attributed networks. Therefore, detecting overlapping communities using a combination of nodes attributes and topological structure is challenging. In this paper, we propose an algorithm, called WLNI-LPA, based on label propagation for detecting efficient community structure in the attributed network. WLNI-LPA is an extension of LPA that combines node importance, attributes information, and topology structure to improve the quality of graph partition. In the experiments, we validate the
performance of our method on synthetic weighted networks. Also, a part of the experiment focuses on the impact of detecting significantly overlapping communities in the recommender system to improve the quality of recommendation.
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