Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and
learning from graph-structured data, with applications spanning numerous domains …

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 …

Dynamic networks are used in a wide range of fields, including social network analysis,
recommender systems and epidemiology. Representing complex networks as structures …

Automated co-located human–human interaction analysis has been addressed by the use of
nonverbal communication as measurable evidence of social and psychological phenomena …

In an online social network, link recommendations are a way for users to discover relevant
links to people they may know, thereby potentially increasing their engagement on the …

Unsupervised representation learning for dynamic graphs has attracted a lot of research
attention in recent years. Compared with static graph, the dynamic graph is a …

Dynamic Graph Neural Networks (GNNs) combine temporal information with GNNs to
capture structural, temporal, and contextual relationships in dynamic graphs simultaneously …

In recent years, Dynamic Graph (DG) representations have been increasingly used for
modeling dynamic systems due to their ability to integrate both topological and temporal …

Temporal graph entity alignment aims at finding the equivalent entity pairs across different
temporal knowledge graphs (TKGs). Primarily methods mainly utilize a time-aware and …

In graph-level representation learning tasks, graph neural networks have received much
attention for their powerful feature learning capabilities. However, with the increasing scales …