| Full text | |
| Author(s): |
Tautenhain, Camila P. S.
[1]
;
Nascimento, V, Maria C.
Total Authors: 2
|
| Affiliation: | [1] V, Univ Fed Sao Paulo, Inst Ciencia & Tecnol, Sao Jose Dos Campos - Brazil
Total Affiliations: 1
|
| Document type: | Journal article |
| Source: | EXPERT SYSTEMS WITH APPLICATIONS; v. 141, MAR 1 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 16/22688-2 - Spectral Theory for Analysing Graph Clustering |
| Grantee: | Camila Pereira dos Santos Tautenhain |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 15/21660-4 - Hibridizing heuristic and exact methods to approach combinatorial optimization problems |
| Grantee: | Mariá Cristina Vasconcelos Nascimento Rosset |
| Support Opportunities: | Regular Research Grants |