Abstract
Graph structures have attracted much research attention for carrying complex relational information. Based on graphs, many algorithms and tools are proposed and developed for dealing with real-world tasks such as recommendation, fraud detection, molecule design, etc. In this paper, we first discuss three topics of graph research, i.e., graph mining, graph representations, and graph neural networks (GNNs). Then, we introduce the definitions of natural dynamics and artificial dynamics in graphs, and the related works of natural and artificial dynamics about how they boost the aforementioned graph research topics, where we also discuss the current limitation and future opportunities.
Subject
Artificial Intelligence,Information Systems,Computer Science (miscellaneous)
Cited by
3 articles.
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1. Investigating Natural and Artificial Dynamics in Graph Data Mining and Machine Learning;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21
2. Node Classification Beyond Homophily: Towards a General Solution;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04
3. Kernel Ridge Regression-Based Graph Dataset Distillation;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04