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 …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

Temporal networks serve as abstractions of many real-world dynamic systems. These
networks typically evolve according to certain laws, such as the law of triadic closure, which …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Abstract Representation learning over graph structured data has been mostly studied in
static graph settings while efforts for modeling dynamic graphs are still scant. In this paper …

Lateral movement is a key stage of system compromise used by advanced persistent
threats. Detecting it is no simple task. When network host logs are abstracted into discrete …

The evolutionary behavior of temporal networks has gained the attention of researchers with
its ubiquitous applications in a variety of real-world scenarios. Learning evolutionary …

Link prediction is an important task in data mining, which has widespread applications in
social network research. Given a social network, the objective of this task is to predict future …

Dynamic networks are ubiquitous. Link prediction in dynamic networks has attracted
tremendous research interests. Many models have been developed to predict links that may …

Although graph representation learning has been studied extensively in static graph
settings, dynamic graphs are less investigated in this context. This paper proposes a novel …