Structural Clustering of Multi-Layer Graphs

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...

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Published in:IEEE transactions on knowledge and data engineering Vol. 37; no. 9; pp. 5639 - 5653
Main Authors: Liu, Xudong, Zou, Zhaonian, Wang, Run-An, Liu, Dandan
Format: Journal Article
Language:English
Published: IEEE 01.09.2025
Subjects:
Artificial intelligence
Bridges
Clustering algorithms
community detection
Data mining
Detection algorithms
Indexes
Multi-layer graph
Periodic structures
Proteins
Social networking (online)
structural clustering
Training
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Abstract 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 multi-layer 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 preferences 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.
AbstractList 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 multi-layer 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 preferences 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.
Author Zou, Zhaonian
Wang, Run-An
Liu, Xudong
Liu, Dandan
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Snippet Multi-layer graphs have emerged as a new representation of multi-faceted relationships between entities in the real world. Community detection on multi-layer...
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SubjectTerms Artificial intelligence
Bridges
Clustering algorithms
community detection
Data mining
Detection algorithms
Indexes
Multi-layer graph
Periodic structures
Proteins
Social networking (online)
structural clustering
Training
Title Structural Clustering of Multi-Layer Graphs
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