With the widespread use of information technologies, information networks are becoming
increasingly popular to capture complex relationships across various disciplines, such as …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Network embedding leverages the node proximity manifested to learn a low-dimensional
node vector representation for each node in the network. The learned embeddings could …

Graph is a universe data structure that is widely used to organize data in real-world. Various
real-word networks like the transportation network, social and academic network can be …

Graph representation learning is to learn universal node representations that preserve both
node attributes and structural information. The derived node representations can be used to …

Dealing with relational data always required significant computational resources, domain
expertise and task-dependent feature engineering to incorporate structural information into a …

Network embedding aims to learn a latent, low-dimensional vector representations of
network nodes, effective in supporting various network analytic tasks. While prior arts on …

Networks have provided extremely successful models of data and complex systems. Yet, as
combinatorial objects, networks do not have in general intrinsic coordinates and do not …

The proliferation of networked data in various disciplines motivates a surge of research
interests on network or graph mining. Among them, node classification is a typical learning …

Dynamic graph embedding learns representation vectors for vertices and edges in a graph
that evolves over time. We aim to capture and embed the evolution of vertices' temporal …