Abstract Graph Neural Networks (GNNs) have received considerable attention in recent
years due to their unique ability to model both topologies and semantics in the graphs. In …

Inspired by the success of Contrastive Learning (CL) in computer vision and natural
language processing, Graph Contrastive Learning (GCL) has been developed to learn …

Contrastive learning (CL) has emerged as a powerful approach for self-supervised learning.
However, it suffers from sampling bias, which hinders its performance. While the mainstream …

Temporal graph is an abstraction for modeling dynamic systems that consist of evolving
interaction elements. In this paper, we aim to solve an important yet neglected problem--how …

Fully connected Graph Transformers (GT) have rapidly become prominent in the static graph
community as an alternative to Message-Passing models, which suffer from a lack of …

In recent years, the Graph Transformer has demonstrated superiority on various graph-level
tasks by facilitating global interactions among nodes. However, as for node-level tasks, the …

Over the last few years Literature-based Discovery (LBD) has regained popularity as a
means to enhance the scientific research process. The resurgent interest has spurred the …

Abstract Objectives Hypothesis Generation (HG) is a task that aims to uncover hidden
associations between disjoint scientific terms, which influences innovations in prevention …