Social Theory Aware Influence Maximization Approach for Target Community Detection

Targeted community detection is a network analysis involving identifying specific subsets or clusters of nodes (communities) within a more extensive network that are particularly relevant to a topic or phenomenon. Unlike traditional community detection methods, which aim to identify all communities within a network, targeted community detection methods focus on finding a specific community or set of communities based on some pre-defined criteria. Nevertheless, all previous effort either mainly disregards the external effect of the community or is not ”goal-based,” i.e., inappropriate for the goal request. To address this issue, we present an attribute network-oriented target community discovery technique that blends user interest preferences and community influence to mine high-quality communities associated with user preferences and have the most influence. To capture the attribute subspace weight of the possible target community and mine user preferences, the greatest k-cluster, including sample nodes, is first mined as the core of the potential target community by synthesising the node structure and attribute information. Then the fusion of the maximum k-cluster with the sample nodes is mined as the core of the potential target community. Finally, the fusion of the sample nodes defines the community’s external influence score quantifica-tion technique and combines the community quality function value with the exterior influence score. Finally, all possible target communities are ranked by their influence score. As a result, the communities with the highest overall quality become the target communities. In addition, a 2-fold pruning procedure is intended to increase the method’s performance and efficiency while calculating the attribute subspace weights of the largest k-cluster. Experimental results on synthetic and actual network datasets validate the efficiency and efficacy of the suggested strategy.