ToĪddress this challenge, we build arXiv4TGC, a set of novel academic datasets
It makesĮvaluating models for large-scale temporal graph clustering challenging. In other words, mostĮxisting temporal graph datasets are in small sizes, and even large-scaleĭatasets contain only a limited number of available node labels. Graph datasets to evaluate clustering performance. Significant problem: the lack of suitable and reliable large-scale temporal However, the development of TGC is currently constrained by a Its focus is on node clustering on temporal graphs, and it offers greaterįlexibility for large-scale graph structures due to the mechanism of temporal Download a PDF of the paper titled arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering, by Meng Liu and 5 other authors Download PDF Abstract: Temporal graph clustering (TGC) is a crucial task in temporal graph learning.
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