# arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering

arXiv: https://arxiv.org/abs/2306.04962 · HTML: https://mlsanity.com/paper/2306.04962/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [Inductive Representation Learning on Temporal Graphs](https://mlsanity.com/paper/2002.07962/) (2020)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks](https://mlsanity.com/paper/2101.05974/) (2021)
- [A Survey on Network Embedding](https://mlsanity.com/paper/1711.08752/) (2017)
- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [Motifs in Temporal Networks](https://mlsanity.com/paper/1612.09259/) (2016)
- [Deep Graph Clustering via Dual Correlation Reduction](https://mlsanity.com/paper/2112.14772/) (2021)
- [TREND: TempoRal Event and Node Dynamics for Graph Representation Learning](https://mlsanity.com/paper/2203.14303/) (2022)
- [Dink-Net: Neural Clustering on Large Graphs](https://mlsanity.com/paper/2305.18405/) (2023)
- [Towards Fine-Grained Reasoning for Fake News Detection](https://mlsanity.com/paper/2110.15064/) (2021)
- [Deep Temporal Graph Clustering](https://mlsanity.com/paper/2305.10738/) (2023)
- [Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influences](https://mlsanity.com/paper/2110.00267/) (2021)
- [Predicting Information Pathways Across Online Communities](https://mlsanity.com/paper/2306.02259/) (2023)
- [Code Recommendation for Open Source Software Developers](https://mlsanity.com/paper/2210.08332/) (2022)
- [On the Equivalence Between Temporal and Static Graph Representations for Observational Predictions](https://mlsanity.com/paper/2103.07016/) (2021)
- [SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning](https://mlsanity.com/paper/2304.10297/) (2023)

## Built on by (most cited first, 3 of 3)

- [Reinforcement Graph Clustering with Unknown Cluster Number](https://mlsanity.com/paper/2308.06827/) (2023)
- [Accelerating Dynamic Graph Clustering on GPU Architectures with cuGraph](https://mlsanity.com/paper/2608.03695/) (2026)
- [Learning and Clustering on Temporal Graphs: Principles, Primitives, and Pooling](https://mlsanity.com/paper/2608.03696/) (2026)

## Abstract (arXiv)

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.