Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …

Network representation learning (NRL) advances the conventional graph mining of social
networks, knowledge graphs, and complex biomedical and physics information networks …

Deep learning has been shown to be successful in a number of domains, ranging from
acoustics, images, to natural language processing. However, applying deep learning to the …

Abstract Graph Contrastive Learning (GCL), learning the node representations by
augmenting graphs, has attracted considerable attentions. Despite the proliferation of …

Since real-world objects and their interactions are often multi-modal and multi-typed,
heterogeneous networks have been widely used as a more powerful, realistic, and generic …

Network embedding, which aims to represent network data in a low-dimensional space, has
been commonly adopted for analyzing heterogeneous information networks (HIN). Although …

Bipartite graph embedding has recently attracted much attention due to the fact that bipartite
graphs are widely used in various application domains. Most previous methods, which adopt …

Code embedding, as an emerging paradigm for source code analysis, has attracted much
attention over the past few years. It aims to represent code semantics through distributed …

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 …

Node classification is an important problem on graphs. While recent advances in graph
neural networks achieve promising performance, they require abundant labeled nodes for …