RESEARCH ON DYNAMIC COMMUNITY DETECTION BASED ON A CONSENSUS COMMUNITY MULTIOBJECTIVE CUCKOO ALGORITHM - Liu Yajie; Md Gapar Md Johar; Ma Yan, (INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY (IJCET), 2023)
Dynamic network community detection reveals patterns in community structure evolution over time and is therefore crucial for critical applications such as social network analysis. Such detection requires maximizing clustering accuracy at the current moment while minimizing results drift between two consecutive moments. The knowledge of the previous moment is the intra-community consensus extracted from the optimal community of the previous moment. Subsequently, the community of the current moment votes on the inter-community consensus of the previous moment during the evolutionary process. A subset of the internal-community consensus that receives high support will be considered the inter-community consensus of the earlier and current moments. The concept of inter-community consensus refers to the knowledge that can be transferred from one moment to another. The community at the current moment inserts inter-community consensus into the evolutionary process and can evolve in a direction similar to that of the community at the previous moment. The cuckoo algorithm guides the evolution process, which influences the community structure through evaluation, update, and mutation events. Experimental results on several artificial networks and real dynamic networks show that the method has higher accuracy and robustness than existing methods.