Graph neural networks (GNNs) aim to learn well-trained representations in a lower-
dimension space for downstream tasks while preserving the topological structures. In recent …

Hypergraphs are an omnipresent data structure used to represent high-order interactions
among entities. Given a hypergraph H wherein nodes are associated with attributes …

With the rapid growth of interaction data, many clustering methods have been proposed to
discover interaction patterns as prior knowledge beneficial to downstream tasks …

A bipartite graph contains inter-set edges between two disjoint vertex sets, and is widely
used to model real-world data, such as user-item purchase records, author-article …

Attributed networks containing entity-specific information in node attributes are ubiquitous in
modeling social networks, e-commerce, bioinformatics, etc. Their inherent network topology …

Multiview attribute graph clustering aims to cluster nodes into disjoint categories by taking
advantage of the multiview topological structures and the node attribute values. However …

Graph representation learning (GRL) is to encode graph elements into informative vector
representations, which can be used in downstream tasks for analyzing graph-structured data …

In recent years, graph neural networks (GNNs) have emerged as a potent tool for learning
on graph-structured data and won fruitful successes in varied fields. The majority of GNNs …

Graph neural networks (GNNs) have emerged as go-to models for node classification in
graph data due to their powerful abilities in fusing graph structures and attributes. However …

Attributed bipartite graphs (ABGs) are an expressive data model for describing the
interactions between two sets of heterogeneous nodes that are associated with rich …