Recently, deep clustering utilizing Graph Neural Networks has shown good performance in
the graph clustering. However, the structure information of graph was underused in existing …

Deep graph clustering, efficiently dividing nodes into multiple disjoint clusters in an
unsupervised manner, has become a crucial tool for analyzing ubiquitous graph data …

Node classification is an important task in many graph-related applications. Recently, graph
neural networks like Graph Convolutional Networks (GCNs) have provided low-dimensional …

Semi-supervised node classification is an important task that aims at classifying nodes
based on the graph structure, node features, and class labels for a subset of nodes. While …

Abstract Adopting Graph Convolutional Networks (GCNs) for transductive node classification
is a hot research direction in artificial intelligence. Vanilla GCNs are primarily under …

Benefiting from the informative expression capability of contrastive representation learning
(CRL), recent multi-modal learning studies have achieved promising clustering …

Many deep graph neural networks perform well in tackling semi-supervised node
classification tasks. However, they often focus on learning a holistic representation for …

Graph node classification with few labeled nodes presents significant challenges due to
limited supervision. Conventional methods often exploit the graph in a transductive learning …

Abstract Heterogeneous Graph Neural Networks represent a powerful approach to
understand and utilize the intricate structures and semantics within complex graphs. When it …

Employing graph neural networks (GNNs) to learn cohesive and discriminative node
representations for clustering has shown promising results in deep graph clustering …