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 …

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 …

Graph Neural Networks (GNNs) have demonstrated great power in many network analytical
tasks. However, graphs (ie, networks) in the real world are usually text-rich, implying that …

Graph clustering is one of the most significant, challenging, and valuable topic in the
analysis of real complex networks. To detect the cluster configuration accurately and …

Multi-view data effectively model and characterize the underlying complex systems, and
multi-view clustering is of great significance for revealing the mechanisms of systems, which …

In this article, we first propose a graph neural network encoding method for the
multiobjective evolutionary algorithm (MOEA) to handle the community detection problem in …

Network embedding aims to learn the low-dimensional representations for the components
in the network while maximally preserving the structure and inherent properties. Its efficiency …

Graph embedding analyzes network data by learning the vector representation of each
vertex in the network, and has attracted widespread attention in recent years. In many real …

Discovering communities is one of the important features of complex networks, as it reveals
the structural features within such networks. Community detection is an optimization …

Most of the existing Graph Neural Networks (GNNs) are deliberately designed for semi-
supervised learning tasks, where supervision information (labelled node) is utilized to …