The sequential recommender (SR) system is a crucial component of modern recommender
systems, as it aims to capture the evolving preferences of users. Significant efforts have …

Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a
fundamental yet challenging task. Benefiting from the powerful representation capability of …

The design of\emph {de novo} functional proteins that bind with specific ligand molecules is
crucial in various domains like therapeutics and bio-engineering. One vital yet challenging …

Temporal graph learning aims to generate high-quality representations for graph-based
tasks with dynamic information, which has recently garnered increasing attention. In contrast …

Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised
node classification in recent years. However, the problem of insufficient supervision …

Antibodies are crucial proteins produced by the immune system in response to foreign
substances or antigens. The specificity of an antibody is determined by its complementarity …

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 Neural Networks (GNNs) are widely employed to derive meaningful node
representations from graphs. Despite their success, deep GNNs frequently grapple with the …

Various information in different modalities in an intuitive way in multi-modal knowledge
graphs (MKGs), which are utilized in different downstream tasks, like recommendation …

Contrastive deep graph clustering (CDGC) leverages the power of contrastive learning to
group nodes into different clusters. The quality of contrastive samples is crucial for achieving …