Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …

This paper targets at improving the generalizability of hypergraph neural networks in the low-
label regime, through applying the contrastive learning approach from images/graphs (we …

Contrastive learning (CL), which can extract the information shared between different
contrastive views, has become a popular paradigm for vision representation learning …

Graph contrastive learning (GCL), as an emerging self-supervised learning technique on
graphs, aims to learn representations via instance discrimination. Its performance heavily …

In contrastive learning, the choice of" view" controls the information that the representation
captures and influences the performance of the model. However, leading graph contrastive …

This paper proposes a novel contrastive learning framework, called FOCAL, for extracting
comprehensive features from multimodal time-series sensing signals through self …

Abstract Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph
representation learning method. However, it has been shown that GCL is vulnerable to …

Spatial-temporal graph learning has emerged as the state-of-the-art solution for modeling
structured spatial-temporal data in learning region representations for various urban sensing …

In the big data era, the relationship between entities becomes more complex. Therefore,
graph (or network) data attracts increasing research attention for carrying complex relational …