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 …

Graph neural networks (GNNs) have been playing important roles in various graph-related
tasks. However, most existing GNNs are based on the assumption of homophily, so they …

Malicious software (malware) poses an increasing threat to the security of communication
systems as the number of interconnected mobile devices increases exponentially. While …

Since most of the real-world networks are heterogeneous, existing methods cannot
characterize the roles of nodes in heterogeneous networks. The neighborhood structure of …

Graph Neural Networks (GNNs) such as Graph Convolutional Networks (GCNs) can
effectively learn node representations via aggregating neighbors based on the relation …

User representation learning is one prominent and critical task of user analysis on social
networks, which derives conceptual user representations to improve the inference of user …

Aspect-level sentiment classification (ASC) is designed to identify the sentiment orientation
of given aspect terms in a sentence. Previous neural networks have used attention …

Graph neural networks (GNNs) have emerged as a powerful model to capture critical graph
patterns. Instead of treating them as black boxes in an end-to-end fashion, attempts are …

Graph collaborative filtering (GCF) has achieved exciting recommendation performance with
its ability to aggregate high-order graph structure information. Recently, contrastive learning …

User modeling is critical for developing personalized services in industry. A common way for
user modeling is to learn user representations that can be distinguished by their interests or …