A Local 2-Order Mutual Information Metric Approach for Service Community Detection in Complex Service Networks
Kexin Zhang, Qing Gao, Jinhu Lü, MACIEJ J. OGORZAŁEK, Yue Deng · IEEE Transactions on Artificial Intelligence · 2025
This paper presents a novel local 2-order mutual information metric (L2oMI) approach for service community detection, addressing the limitations of traditional methods in dealing with large scale and complex service networks. Firstly, a local 2-order mutual information metric module is proposed to measure the inner-distance between each pair of service stations from different-sized receptive fields in complex service networks, based on which useful semantic node embeddings are obtained for service community detection. Next, a multi-order subgraph information bottleneck for topology module is introduced for the L2oMI approach as a topology constraint to improve the effectiveness of node embedding. Furthermore, a hierarchical interaction learning module is proposed to learn the complicated interactions between pairs of service station and to encode their node features in complex service networks. Numerical experiments using several service network datasets demonstrate the efficacy of the proposed approach.