In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …

This review provides a comprehensive overview of the state-of-the-art methods of graph-
based networks from a deep learning perspective. Graph networks provide a generalized …

Financial time series analysis plays a central role in hedging market risks and optimizing
investment decisions. This is a challenging task as the problems are always accompanied …

State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the
graph and model sizes. On large graphs, increasing the model depth often means …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

With the rapid proliferation of scientific literature, versatile academic knowledge services
increasingly rely on comprehensive academic graph mining. Despite the availability of …

Conventional attacks of insider employees and emerging APT are both major threats for the
organizational information system. Existing detections mainly concentrate on users' behavior …

Graph Neural Networks (GNNs) have gained significant attention in the recent past, and
become one of the fastest growing subareas in deep learning. While several new GNN …

Abstract Graph Convolutional Networks (GCNs) are known to suffer from performance
degradation as the number of layers increases, which is usually attributed to over …

Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such
as social network analysis, financial fraud detection, and drug discovery. As a central …