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 …

Evolutionary neural architecture search (ENAS) and differentiable architecture search
(DARTS) are all prominent algorithms in neural architecture search, enabling the automated …

Multi-relational graph clustering has demonstrated remarkable success in uncovering
underlying patterns in complex networks. Representative methods manage to align different …

The opioid crisis has been one of the most critical society concerns in the United States.
Although the medication assisted treatment (MAT) is recognized as the most effective …

Contrastive Learning (CL)-based recommender systems have gained prominence in the
context of Heterogeneous Graph (HG) due to their capacity to enhance the consistency of …

Abstract In recent years, Graph Neural Networks (GNNs), an emerging technology for
learning from graph-structured data, have attracted much attention. Despite the widespread …

With the explosive growth of user-generated content, multi-view learning has become a
rapidly growing direction in pattern recognition and data analysis areas. Due to the …

Inspired by the success of foundation models in applications such as ChatGPT, as graph
data has been ubiquitous, one can envision the far-reaching impacts that can be brought by …

Heterogeneous graph contrastive learning is an effective method to learn discriminative
representations of nodes in heterogeneous graph when the labels are absent. To utilize …

Unsupervised multi-view graph representation learning (UMGRL) aims to capture the
complex relationships in the multi-view graph without human annotations, so it has been …