arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering
Meng Liu Ke Liang Yue Liu Siwei Wang Sihang Zhou Xinwang Liu
Temporal graph clustering (TGC) is a crucial task in temporal graph learning.
Its focus is on node clustering on temporal graphs, and it offers greater
flexibility for large-scale graph structures due to the mechanism of temporal
graph methods. However, the development of TGC is currently constrained by a
significant problem: the lack of suitable and reliable large-scale temporal
graph datasets to evaluate clustering performance. In other words, most
existing temporal graph datasets are in small sizes, and even large-scale
datasets contain only a limited number of available node labels. It makes
evaluating models for large-scale temporal graph clustering challenging. To
address this challenge, we build arXiv4TGC, a set of novel academic datasets
(including arXivAI, arXivCS, arXivMath, arXivPhy, and arXivLarge) for
large-scale temporal graph clustering. In particular, the largest dataset,
arXivLarge, contains 1.3 million labeled available nodes and 10 million
temporal edges. We further compare the clustering performance with typical
temporal graph learning models on both previous classic temporal graph datasets
and the new datasets proposed in this paper. The clustering performance on
arXiv4TGC can be more apparent for evaluating different models, resulting in
higher clustering confidence and more suitable for large-scale temporal graph
clustering. The arXiv4TGC datasets are publicly available at:
https://github.com/MGitHubL/arXiv4TGC.