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 …

Over the past few years, the number and volume of data sources in healthcare databases
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …

The most basic and significant issue in complex network analysis is community detection,
which is a branch of machine learning. Most current community detection approaches, only …

Abstract Space anomaly detection plays a critical role in safeguarding the integrity and
reliability of space systems amid the rising tide of threats. This survey aims to deepen …

Community detection, aiming to group the graph nodes into clusters with dense inner-
connection, is a fundamental graph mining task. Recently, it has been studied on the …

Abstract In modern Online Social Networks (OSNs), the need to detect users' communities
based on their interests and social connections has became a more and more important …

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 …

Recent developments in attributed network clustering combine graph neural networks and
autoencoders for unsupervised learning. Although effective, these techniques suffer from …

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 …

Temporal knowledge graph (TKG) representation learning aims to project entities and
relations in TKGs to a low-dimensional vector space while preserving the evolutionary …