Link prediction finds missing links (in static networks) or predicts the likelihood of future links
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …

Link prediction on dynamic graphs is an important task in graph mining. Existing approaches
based on dynamic graph neural networks (DGNNs) typically require a significant amount of …

Complex networks are mathematical abstractions of real-world systems using sets of nodes
and edges representing the entities and their interactions. Prediction of unknown …

In this article, we aim at developing a user representation learning model to solve the
information diffusion prediction problem in social media. The main idea is to project the …

In the 5G era, data traffic has been growing rapidly. A small number of popular data files may
dominate the network traffic and lead to heavy network congestion. Device-to-Device (D2D) …

Link prediction is a crucial task in dynamic graph learning. Recent advancements in
continuous-time dynamic graph models, primarily by leveraging richer temporal details …

With the spread of Internet and big data research and applications, influence propagation in
networks becomes one of the hot topics in the field of social network analysis in recent …

Data mining in social networks brings an indispensable role for the construction of smart
cities from the perspective of social development. Link prediction is an important task of data …

There is an emerging trend in the Chinese automobile industries that automakers are
introducing exclusive enterprise social networks (EESNs) to expand sales and provide after …

Network analysis has been widely applied in many real-world tasks, such as gene analysis
and targeted marketing. To extract effective features for these analysis tasks, network …