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 …

Few-shot learning (FSL) has received a lot of attention due to its remarkable ability to adapt
to novel classes. Although many techniques have been proposed for FSL, they mostly focus …

Abstract Graph-based Collaborative Filtering (GCF) is widely used in personalized
recommendation systems. However, GCF suffers from a fundamental problem where …

Graph contrastive learning attracts/disperses node representations for similar/dissimilar
node pairs under some notion of similarity. It may be combined with a low-dimensional …

Human actions in video sequences are characterized by the complex interplay between
spatial features and their temporal dynamics. In this paper, we propose novel tensor …

Graph Neural Networks (GNNs) have achieved remarkable success by extending traditional
convolution to learning on non-Euclidean data. The key to the GNNs is adopting the neural …

Graph contrastive learning attracts/disperses node representations for similar/dissimilar
node pairs under some notion of similarity. It may be combined with a low-dimensional …

Even with the luxury of having abundant data, multi-label classification is widely known to be
a challenging task to address. This work targets the problem of multi-label meta-learning …

Network embedding is a fundamental part of many network analysis tasks, including node
classification and link prediction. The existing random walk-based embedding methods aim …