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Graph Neural Network Encoding for Community Detection in Attribute Networks
- Source :
- IEEE Transactions on Cybernetics. 52:7791-7804
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- In this paper, we first propose a graph neural network encoding method for multiobjective evolutionary algorithm to handle the community detection problem in complex attribute networks. In the graph neural network encoding method, each edge in an attribute network is associated with a continuous variable. Through non-linear transformation, a continuous valued vector (i.e. a concatenation of the continuous variables associated with the edges) is transferred to a discrete valued community grouping solution. Further, two objective functions for single- and multi-attribute network are proposed to evaluate the attribute homogeneity of the nodes in communities, respectively. Based on the new encoding method and the two objectives, a multiobjective evolutionary algorithm (MOEA) based upon NSGA-II, termed as continuous encoding MOEA, is developed for the transformed community detection problem with continuous decision variables. Experimental results on single- and multi-attribute networks with different types show that the developed algorithm performs significantly better than some well-known evolutionary and non-evolutionary based algorithms. The fitness landscape analysis verifies that the transformed community detection problems have smoother landscapes than those of the original problems, which justifies the effectiveness of the proposed graph neural network encoding method.
- Subjects :
- FOS: Computer and information sciences
Theoretical computer science
Computer science
Graph neural networks
Fitness landscape
Concatenation
Evolutionary algorithm
Computer Science - Neural and Evolutionary Computing
Computer Science Applications
Human-Computer Interaction
Control and Systems Engineering
Encoding (memory)
Neural Networks, Computer
Neural and Evolutionary Computing (cs.NE)
Enhanced Data Rates for GSM Evolution
Electrical and Electronic Engineering
Algorithms
Software
Information Systems
Subjects
Details
- ISSN :
- 21682275 and 21682267
- Volume :
- 52
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Cybernetics
- Accession number :
- edsair.doi.dedup.....1f888827b1d93aaea9d641bc1667ff61