A Combinatorial Multi-Armed Bandit Based Method for Dynamic Consensus Community Detection in Temporal Networks
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Community detection in temporal networks is an active field of research, which can be leveraged for several strategic decisions, including enhanced group-recommendation, user behavior prediction, and evolution of user interaction patterns in relation to real-world events. Recent research has shown that combinatorial multi-armed bandit (CMAB) is a suitable methodology to address the problem of dynamic consensus community detection (DCCD), i.e., to compute a single community structure that is conceived to be representative of the knowledge available from community structures observed at the different time steps.
In this paper, we propose a CMAB-based method, called CreDENCE, to solve the DCCD problem. Unlike existing approaches, our algorithm is designed to provide a solution, i.e., dynamic consensus community structure, that embeds both long-term changes in the community formation and newly observed community structures. Experimental evaluation based on publicly available real-world and ground-truth-oriented synthetic networks, with different structure and evolution rate, has confirmed the meaningfulness and key benefits of the proposed method, also against competitors based on evolutionary or consensus approaches.
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