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An ensemble based on a bi-objective evolutionary spectral algorithm for graph clustering
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DOI：10.1016/j.eswa.2019.112911.png)
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摘要
En 中文
Graph clustering is a challenging pattern recognition problem whose goal is to identify vertex partitions with high intra-group connectivity. This paper investigates a bi-objective problem that maximizes the number of intra-cluster edges of a graph and minimizes the expected number of inter-cluster edges in a random graph with the same degree sequence as the original one. The difference between the two investigated objectives is the definition of the well-known measure of graph clustering quality: the modularity. We introduce a spectral decomposition hybridized with an evolutionary heuristic, called MOSpecG, to approach this bi-objective problem and an ensemble strategy to consolidate the solutions found by MOSpecG into a final robust partition. The results of computational experiments with real and artificial LFR networks demonstrated a significant improvement in the results and performance of the introduced method in regard to another bi-objective algorithm found in the literature. The crossover operator based on the geometric interpretation of the modularity maximization problem to match the communities of a pair of individuals was of utmost importance for the good performance of MOSpecG. Hybridizing spectral graph theory and intelligent systems allowed us to define significantly high-quality community structures. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Graph clustering
Community detection
Evolutionary heuristic
Multi-objective optimization
Modularity maximization
Spectral decomposition
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期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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