Y Zhou, H Zheng, X Huang, S Hao, D Li… - ACM Transactions on …, 2022 - dl.acm.org
Graph neural networks provide a powerful toolkit for embedding real-world graphs into low- dimensional spaces according to specific tasks. Up to now, there have been several surveys …
Abstract We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine …
Understanding crowd motion dynamics is critical to real-world applications, eg, surveillance systems and autonomous driving. This is challenging because it requires effectively …
Learning node representations in graphs is important for many applications such as link prediction, node classification, and community detection. Existing graph representation …
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 …
The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (ie, embeddings) of entities and relations. However …
Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive abilities by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to …
In this paper, we study the problem of learning probabilistic logical rules for inductive and interpretable link prediction. Despite the importance of inductive link prediction, most …
Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems and epidemiology. Representing complex networks as structures …