Embedding static graphs in low-dimensional vector spaces plays a key role in network
analytics and inference, supporting applications like node classification, link prediction, and …

Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static)
graph-structured data. However, many real-world systems are dynamic in nature, since the …

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

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

Detecting rumors on social media has become particular important due to the rapid
dissemination and adverse impacts on our lives. Though a set of rumor detection models …

Graph neural networks (GNNs) have achieved strong performance in various applications.
In the real world, network data is usually formed in a streaming fashion. The distributions of …

Social events provide valuable insights into group social behaviors and public concerns and
therefore have many applications in fields such as product recommendation and crisis …

Modeling concurrent events of multiple types and their involved actors from open-source
social sensors is an important task for many domains such as health care, disaster relief …

Sampling methods (eg, node-wise, layer-wise, or subgraph) has become an indispensable
strategy to speed up training large-scale Graph Neural Networks (GNNs). However, existing …

Network embedding has emerged as a new learning paradigm to embed complex network
into a low-dimensional vector space while preserving node proximities in both network …