# Graph Neural Network Encoding for Community Detection in Attribute Networks

arXiv: https://arxiv.org/abs/2006.03996 · HTML: https://mlsanity.com/paper/2006.03996/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2020.

## Built on by (most cited first, 17 of 17)

- [A Comprehensive Survey on Community Detection with Deep Learning](https://mlsanity.com/paper/2105.12584/) (2021)
- [A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges](https://mlsanity.com/paper/2210.12089/) (2022)
- [Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment](https://mlsanity.com/paper/2302.07491/) (2023)
- [DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization](https://mlsanity.com/paper/2312.12697/) (2023)
- [Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective](https://mlsanity.com/paper/2406.14288/) (2024)
- [A Learning Convolutional Neural Network Approach for Network Robustness Prediction](https://mlsanity.com/paper/2203.10552/) (2022)
- [VertexSerum: Poisoning Graph Neural Networks for Link Inference](https://mlsanity.com/paper/2308.01469/) (2023)
- [Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning](https://mlsanity.com/paper/2305.02901/) (2023)
- [Robustness of shallow graph embedding methods for community detection](https://mlsanity.com/paper/2405.00636/) (2024)
- [Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations](https://mlsanity.com/paper/2105.06715/) (2021)
- [Superpixel perception graph neural network for intelligent defect detection of aero-engine blade](https://mlsanity.com/paper/2210.07539/) (2022)
- [A Survey on Machine Learning Solutions for Graph Pattern Extraction](https://mlsanity.com/paper/2204.01057/) (2022)
- [A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty](https://mlsanity.com/paper/2304.04497/) (2023)
- [Migrate Demographic Group For Fair GNNs](https://mlsanity.com/paper/2306.04212/) (2023)
- [RDSA: A Robust Deep Graph Clustering Framework via Dual Soft Assignment](https://mlsanity.com/paper/2410.21745/) (2024)
- [Let's Grow an Unbiased Community: Guiding the Fairness of Graphs via New Links](https://mlsanity.com/paper/2508.15499/) (2025)
- [Graph Integrated Transformers for Community Detection in Social Networks](https://mlsanity.com/paper/2601.04367/) (2026)

## Abstract (arXiv)

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.