Deep graph clustering, which aims to group the nodes of a graph into disjoint clusters with
deep neural networks, has achieved promising progress in recent years. However, the …

Unsupervised graph representation learning (UGRL) has drawn increasing research
attention and achieved promising results in several graph analytic tasks. Relying on the …

Real-world graphs generally have only one kind of tendency in their connections. These
connections are either homophilic-prone or heterophily-prone. While graphs with homophily …

Most existing deep learning models are trained based on the closed-world assumption,
where the test data is assumed to be drawn iid from the same distribution as the training …

Graph Neural Networks (GNNs) have exhibited impressive performance in many graph
learning tasks. Nevertheless, the performance of GNNs can deteriorate when the input …

While graph neural networks (GNNs) dominate the state-of-the-art for exploring graphs in
real-world applications, they have been shown to be vulnerable to a growing number of …

Recommender systems are essential to various fields, eg, e-commerce, e-learning, and
streaming media. At present, graph neural networks (GNNs) for session-based …

Intent learning, which aims to learn users' intents for user understanding and item
recommendation, has become a hot research spot in recent years. However, the existing …

Graph similarity learning is a significant and fundamental issue in the theory and analysis of
graphs, which has been applied in a variety of fields, including object tracking …

Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous
nodes from graph-structured data in various domains such as medicine, social networks …