WebGraph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compact graph embedding, upon which classic clustering methods like k -means or spectral clustering algorithms are applied. WebIn this paper, we propose a clustering-directed deep learning approach, Deep Neighbor-aware Embedded Node Clustering ( DNENC for short) for clustering graph data. Our method focuses on attributed graphs to sufficiently explore the two sides of …
Clustering Social Networks - Stanford University
WebMay 10, 2024 · [Submitted on 10 May 2024] Deep Graph Clustering via Mutual Information Maximization and Mixture Model Maedeh Ahmadi, Mehran Safayani, Abdolreza Mirzaei … WebApr 3, 2024 · A Deep Fusion Clustering Network (DFCN) is proposed, in which an interdependency learning-based Structure and Attribute Information Fusion (SAIF) … incctv.cn
[2005.02372] Community Detection Clustering via Gumbel Softmax …
WebFeb 1, 2024 · Graph clustering aims to divide nodes of a graph into several disjoint groups and has been widely applied in many real-world scenarios, for example, social networks [1], [2], citation networks [3], protein-protein interaction networks [4], [5]. To achieve promising performance in clustering tasks, the quality of representation is critical. WebAug 24, 2024 · The DGENFS model consists of a Feature Graph Autoencoder (FGA) module, a Structure Graph Attention Network (SGAT) module, and a Dual Self … WebSep 1, 2024 · We propose a deep geometric subspace clustering network, to first embed into low-dimensional latent feature space through graph convolutional layers, using graph node connection structure and content features; and then separate similar graph nodes using latent embeddings through self-expression. inclusivity in architecture