Structural Clustering of Multi-Layer Graphs
Xudong Liu, Zhaonian Zou, Run-An Wang, Dandan Liu · IEEE Transactions on Knowledge and Data Engineering · 2025
Multi-layer graphs have emerged as a new representation of multi-faceted relationships between entities in the real world. Community detection on multi-layer graphs has been investigated to gain deeper insights into the modular structures of real-world graphs. As an effective and efficient approach to community detection, structural clustering has been investigated on single-layer graphs. However, it has been overlooked in the study of community detection on multi-layer graphs. In this paper, we give a formulation of structural clustering on multilayer graphs for the first time. Two polynomial-time algorithms are proposed to solve the problem. Furthermore, two indexes, namely the core index and the interval index, with respective peferences to time efficiency and space efficiency, are designed to improve the efficiency of the algorithms. The experiments demonstrate the effectiveness of structural clustering in improving the quality of community detection results on multi-layer graphs. The experiments also verify the improvement in running time due to the use of the proposed indexes.