Graph is an important data representation which appears in a wide diversity of real-world
scenarios. Effective graph analytics provides users a deeper understanding of what is …

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

Graph embedding is an effective method to represent graph data in a low dimensional
space for graph analytics. Most existing embedding algorithms typically focus on preserving …

Graph analytics can lead to better quantitative understanding and control of complex
networks, but traditional methods suffer from the high computational cost and excessive …

Embedding a web-scale information network into a low-dimensional vector space facilitates
tasks such as link prediction, classification, and visualization. Past research has addressed …

Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph-
analytics tasks like link prediction and graph clustering. Most approaches on graph …

Recently, the surge in popularity of the Internet of Things (IoT), mobile devices, social media,
etc., has opened up a large source for graph data. Graph embedding has been proved …

Abstract Graph Neural Networks (GNNs) have achieved excellent performance of graph
representation learning and attracted plenty of attentions in recent years. Most of GNNs aim …

Attributed networks are ubiquitous in the real world, such as social networks. Therefore,
many researchers take the node attributes into consideration in the network representation …

Network embedding in heterogeneous information networks (HINs) is a challenging task,
due to complications of different node types and rich relationships between nodes. As a …