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 …

Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

Graph neural network, as a powerful graph representation technique based on deep
learning, has shown superior performance and attracted considerable research interest …

Abstract Semi-supervised clustering (SSC), a technique integrating semi-supervised
learning and clustering analysis, incorporates the given prior information (eg, class labels …

Retrieving, analyzing, and processing large data can be challenging. An effective and
efficient mechanism for overcoming these challenges is to cluster the data into a compact …

Heterogeneous information network (HIN) embedding aims at learning the low-dimensional
representation of nodes while preserving structure and semantics in a HIN. Existing methods …

As one of the major frauds in financial services, cash-out fraud is that users pursue cash
gains with illegal or insincere means. Conventional solutions for the cash-out user detection …

Online underground forums have been widely used by cybercriminals to exchange
knowledge and trade in illicit products or services, which have played a central role in the …

Abstract Semi-Supervised Graph Clustering (SSGC) has emerged as a pivotal field at the
intersection of graph clustering and semi-supervised learning (SSL), offering innovative …

Community detection in attributed graph is of great application value and many related
methods have been continually presented. However, existing methods for community …