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 …

Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …

As communities represent similar opinions, similar functions, similar purposes, etc.,
community detection is an important and extremely useful tool in both scientific inquiry and …

Abstract Graph Convolutional Network (GCN) has experienced great success in graph
analysis tasks. It works by smoothing the node features across the graph. The current GCN …

Community detection is a fundamental problem in machine learning. While deep learning
has shown great promise in many graphrelated tasks, developing neural models for …

Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as
powerful methods for link prediction. Their performances are less impressive on community …

Graph clustering, a fundamental and challenging task in graph mining, aims to classify
nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning …

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 …

In the first days of social networking, the typical view of a community was a set of user
profiles of the same interests and likes, and this community kept enlarging by searching …

We propose a general view that demonstrates the relationship between network embedding
approaches and matrix factorization. Unlike previous works that present the equivalence for …