iMGC: Interactive Multiple Graph Clustering With Constrained Laplacian Rank

• Published in: IEEE transactions on human-machine systems Vol. 53; no. 2; pp. 427 - 437
• Main Authors: Zhou, Zhiguang, Sun, Ling, Wang, Haoxuan, Yu, Wanghao, Liu, Yuhua, Zhang, Xiang, Wang, Yigang, Chen, Wei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Clustering; Correlation; Data clustering and aggregation; Graph representations; graph/network and tree data; Graphical representations; Graphs; Junctions; Laplace equations; Layout; Measurement; Nonhomogeneous media; Visualization
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2022.3227181

Numerous graph clustering methods have been proposed to explore aggregation structures across multiple graphs. In these methods, single-graph features are merely considered or multigraph features are simply weighted, which are insufficient for the construction of reasonable multiple graph clustering features, since the association information between pairwise graphs is ignored and the varied local correlations might influence the clustering preference. Thus, we propose an interactive multiple graph clustering model, iMGC, in this article, to achieve reasonable multiple graph clustering features, which cannot only express multiple relationships, but also preserve associations of nodes across multiple graphs. First, a unified graph matrix is constructed with the combination of structural differences quantified by graph representation learning, which is further optimized by minimizing the difference of structural characteristics between it and each single graph matrix. Thus, multiple relationships are well integrated and expressed, while the varied local correlations within different graphs are also balanced in the unified graph matrix. Then, a constrained Laplacian rank is applied on the unified graph matrix to generate the unified clustering result directly, which is able to preserve association features across multiple graphs. Furthermore, we provide a set of visualization and interaction interfaces, enabling users to intuitively optimize and evaluate the multiple graph clustering features, and interactively explore the multiple graphs. Case studies and quantitative comparisons based on real-world datasets have demonstrated the effectiveness of iMGC in the clustering performance from various perspectives and exploration of multiple graphs.