Authors:
Aymene Berriche
1
;
Marwa Naïr
1
;
Kamel Yamani
1
;
Mehdi Adjal
1
;
Sarra Bendaho
1
;
Nidhal Chenni
1
;
Fatima Tayeb
2
;
1
and
Malika Bessedik
2
;
1
Affiliations:
1
Ecole Nationale Supérieure d’Informatique (ESI), BP 68M - 16270 Oued Smar, Alger, Algeria
;
2
Laboratoire des Methodes de Conception de Systèmes (LMCS), BP 68M - 16270 Oued Smar, Alger, Algeria
Keyword(s):
Social Networks, Community Detection, Combinatorial Optimization Problem, Beam Search, DeepWalk, Modularity.
Abstract:
In the era of rapidly expanding social networks, community detection within social graphs plays a pivotal role in various applications such as targeted marketing, content recommendations, and understanding social dynamics. Community detection problem consists of finding a strategy for detecting cohesive groups, based on shared interests, choices, and preferences, given a social network where nodes represent users and edges represent interactions between them. In this work, we propose a hybrid method for the community detection problem that encompasses both traditional tree search algorithms and deep learning techniques. We begin by introducing a beam-search algorithm with a modularity-based agglomeration function as a foundation. To enhance its performance, we further hybridize this approach by incorporating DeepWalk embeddings into the process and leveraging a novel similarity metric for community structure assessment. Experimentation on both synthetic and real-world networks demons
trates the effectiveness of our method, particularly excelling in small to medium-sized networks, outperforming widely adopted methods.
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