A comprehensive survey on community detection with deep learning
Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …
connections of a group of members that are different from those in other communities. The …
Graph neural networks in IoT: A survey
The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
Graph auto-encoder via neighborhood wasserstein reconstruction
Multi-view representation model based on graph autoencoder
J Li, G Lu, Z Wu, F Ling - Information Sciences, 2023 - Elsevier
Graph representation learning is a hot topic in non-Euclidean data in various domains, such
as social networks, biological networks, etc. When some data labels are missing, graph …
as social networks, biological networks, etc. When some data labels are missing, graph …
Simple and effective graph autoencoders with one-hop linear models
Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE)
emerged as powerful node embedding methods, with promising performances on …
emerged as powerful node embedding methods, with promising performances on …
Probabilistic community detection in social networks
The detection of community structures is a very crucial research area. The problem of
community detection has received considerable attention from a large portion of the …
community detection has received considerable attention from a large portion of the …