Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to
be connected, has been commonly believed to be the main reason for the superiority of …

Twitter bot detection has become an increasingly important task to combat misinformation,
facilitate social media moderation, and preserve the integrity of the online discourse. State-of …

Heterogeneous graph neural networks (HGNNs) have the powerful capability to embed rich
structural and semantic information of a heterogeneous graph into node representations …

Abstract Temporal Knowledge Graph (TKG) reasoning aims to predict future facts based on
historical data. However, due to the limitations in construction tools and data sources, many …

Heterogeneous graph learning aims to capture complex relationships and diverse relational
semantics among entities in a heterogeneous graph to obtain meaningful representations …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

By jointly modeling user-item interactions and knowledge graph (KG) information, KG-based
recommender systems have shown their superiority in alleviating data sparsity and cold start …

Graph neural networks (GNNs) and heterogeneous graph neural networks (HGNNs) are
prominent techniques for homogeneous and heterogeneous graph representation learning …

Recent studies have highlighted the limitations of message-passing based graph neural
networks (GNNs), eg, limited model expressiveness, over-smoothing, over-squashing, etc …

Session-based recommendation aims to predict a user's next action based on previous
actions in the current session. The major challenge is to capture authentic and complete …