Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static)
graph-structured data. However, many real-world systems are dynamic in nature, since the …

Graph neural networks generalize conventional neural networks to graph-structured data
and have received widespread attention due to their impressive representation ability. In …

Graph contrastive learning (GCL) improves graph representation learning, leading to SOTA
on various downstream tasks. The graph augmentation step is a vital but scarcely studied …

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 …

Representation learning over temporal networks has drawn considerable attention in recent
years. Efforts are mainly focused on modeling structural dependencies and temporal …

In large-scale recommender systems, the user-item networks are generally scale-free or
expand exponentially. For the representation of the user and item, the latent features (aka …

The non-Euclidean geometry of hyperbolic spaces has recently garnered considerable
attention in the realm of representation learning. Current endeavors in hyperbolic …

Considering the prevalence of the power-law distribution in user-item networks, hyperbolic
space has attracted considerable attention and achieved impressive performance in the …

Aiming to alleviate data sparsity and cold-start problems of tradi-tional recommender
systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has …

Hyperbolic geometry have shown significant potential in modeling complex structured data,
particularly those with underlying tree-like and hierarchical structures. Despite the …