2023/11/21 by Abdelfateh Bekkaira, Bekkair, Abdelfateh, Slimane Bellaouar +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Neuroscience · #68T07 #Artificial Intelligence (cs.AI) #Bioinformatics and Genomic Networks #Computation and Language (cs.CL) #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Gene Regulatory Network Analysis #I.2.4
paper · pdf · doi:10.48550/arxiv.2311.12986
openalex publication_date 2023/11/21 · openalex created_date 2023/11/24 · openalex updated_date 2026/07/28
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 Graph Attention AutoEncoder 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.