WebJin Di, Ge Meng, Zhixuan Li, Wenhuan Lu, and Francoise Fogelman-Soulie. 2024. Using deep learning for community discovery in social networks. In Proceedings of the IEEE 29th International Conference on Tools with Artificial Intelligence. Google Scholar; Santo Fortunato. 2010. Community detection in graphs. Physics Reports 486, 3--5 (2010), 75- …
Graph Community Infomax Semantic Scholar
WebDRGI: Deep Relational Graph Infomax for Knowledge Graph Completion: (Extended Abstract) Abstract: Recently, many knowledge graph embedding models for knowledge … WebGraph representation learning aims at learning low-dimension representations for nodes in graphs, and has been proven very useful in several downstream tasks. In this article, we propose a new model, Graph Community Infomax (GCI), that can adversarial learn representations for nodes in attributed networks. Different from other adversarial ... haband velcro slippers
Community Detection Model Based on Graph Representation and …
WebSep 27, 2024 · We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies … WebSep 27, 2024 · State-of-the-art results, competitive with supervised learning. Abstract: We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of … WebCommunity Detection. 194 papers with code • 11 benchmarks • 9 datasets. Community Detection is one of the fundamental problems in network analysis, where the goal is to find groups of nodes that are, in some sense, more similar to each other than to the other nodes. Source: Randomized Spectral Clustering in Large-Scale Stochastic Block Models. bradford pear tree buy back