Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss

• URL: http://arxiv.org/abs/2311.12986v2
• Date: Fri, 24 Nov 2023 22:24:50 GMT
• Title: Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss
• Authors: Abdelfateh Bekkaira, Slimane Bellaouar and Slimane Oulad-Naoui
• Abstract summary: We introduce a simple, efficient, and clustering-oriented model based on unsupervised textbfGraph Attention textbfAutotextbfEncoder for community detection in attributed networks. The proposed model adeptly learns representations from both the network's topology and attribute information, simultaneously addressing dual objectives: reconstruction and community discovery.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Several natural phenomena and complex systems are often represented as networks. Discovering their community structure is a fundamental task for understanding these networks. Many algorithms have been proposed, but recently, Graph Neural Networks (GNN) have emerged as a compelling approach for enhancing this task.In this paper, we introduce a simple, efficient, and clustering-oriented model based on unsupervised \textbf{G}raph Attention \textbf{A}uto\textbf{E}ncoder for community detection in attributed networks (GAECO). The proposed model adeptly learns representations from both the network's topology and attribute information, simultaneously addressing dual objectives: reconstruction and community discovery. It places a particular emphasis on discovering compact communities by robustly minimizing clustering errors. The model employs k-means as an objective function and utilizes a multi-head Graph Attention Auto-Encoder for decoding the representations. Experiments conducted on three datasets of attributed networks show that our method surpasses state-of-the-art algorithms in terms of NMI and ARI. Additionally, our approach scales effectively with the size of the network, making it suitable for large-scale applications. The implications of our findings extend beyond biological network interpretation and social network analysis, where knowledge of the fundamental community structure is essential.

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