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.

Nonnegative matrix factorization (NMF) is widely used in community discovery because of its effectiveness and easy interpretability. However, most of the existing NMF-based community detection methods are linear. They cannot effectively deal with the nonlinear characteristics of complex networks, resulting in further improvement in community detection performance. Aiming at this problem, a convolution graph network (GCN) enhanced nonlinear NMF community discovery method NMFGCN is proposed. NMFGCN consists of two main modules: GCN and NMF, where GCN is used to learn network node representations, and NMF uses node representations as input to obtain network community representations. In addition, a joint optimization method is proposed to train NMFGCN, which enables NMFGCN to have nonlinear feature representation capabilities and enables GCN and NMF to promote each other and obtain better community segmentation results. Many experiments on artificial synthetic networks and entire networks show that NMFGCN is superior to current NMF-based community discovery methods , thus proving that NMFGCN can improve the performance of NMF community discovery methods. In addition, NMFGCN also outperforms Deep Walk and LINE standard graph representation learning methods.