Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Abstract Graph Neural Networks (GNNs) have shown exceptional performance in the task of
link prediction. Despite their effectiveness, the high latency brought by non-trivial …

The digital deep integration of cyber space, physical space and social space facilitates the
formation of Cyber–Physical–Social Systems (CPSS). Knowledge empowers CPSS to be …

As powerful tools for representation learning on graphs, graph neural networks (GNNs) have
facilitated various applications from drug discovery to recommender systems. Nevertheless …

Graph self-supervised learning has gained increasing attention due to its capacity to learn
expressive node representations. Many pretext tasks, or loss functions have been designed …

Learning informative representations of users and items from the historical interactions is
crucial to collaborative filtering (CF). Existing CF approaches usually model interactions …

Graph Neural Networks (GNNs) have attracted much attention due to their superior learning
capability. Despite the successful applications of GNNs in many areas, their performance …

Explaining machine learning models is an important and increasingly popular area of
research interest. The Shapley value from game theory has been proposed as a prime …

Message passing neural networks (MPNNs) have become a dominant flavor of graph neural
networks (GNNs) in recent years. Yet, MPNNs come with notable limitations; namely, they …

Edge intelligence has arisen as a promising computing paradigm for supporting
miscellaneous smart applications that rely on machine learning techniques. While the …