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 …

The Transformer architecture has achieved remarkable success in a number of domains
including natural language processing and computer vision. However, when it comes to …

Abstract Graph Convolutional Networks (GCNs), aiming to obtain the representation of a
node by aggregating its neighbors, have demonstrated great power in tackling various …

Attributed graph clustering is challenging as it requires joint modelling of graph structures
and node attributes. Recent progress on graph convolutional networks has proved that …

Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

Community detection is a fundamental and widely-studied problem that finds all densely-
connected groups of nodes and well separates them from others in graphs. With the …

Community detection is a fundamental problem in network science with various applications.
The problem has attracted much attention and many approaches have been proposed …

Community detection has emerged during the last decade as one of the most challenging
problems in network science, which has been revisited with network representation learning …

In social network analysis, community detection is a basic step to understand the structure,
function and semantics of networks. Some conventional community detection methods may …