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20242026
most citedPyG 2.0: Scalable Learning on Real World Graphs

8 citations · 21 across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.LG2026

KumoRFM-2: Scaling Foundation Models for Relational Learning

Valter Hudovernik, Federico López, Vid Kocijan +4

We introduce KumoRFM-2, the next iteration of a pre-trained foundation model for relational data. KumoRFM-2 supports in-context learning as well as fine-tuning and is applicable to…

cs.LG2025

TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs

Jacob Chmura, Shenyang Huang, Tran Gia Bao Ngo +7

Well-designed open-source software drives progress in Machine Learning (ML) research. While static graph ML enjoys mature frameworks like PyTorch Geometric and DGL, ML for temporal…

cs.LG2025★ 8 cited

PyG 2.0: Scalable Learning on Real World Graphs

Matthias Fey, Jinu Sunil, Akihiro Nitta +10

PyG (PyTorch Geometric) has evolved significantly since its initial release, establishing itself as a leading framework for Graph Neural Networks. In this paper, we present Pyg 2.0…

cs.LG2025★ 1 cited

Relational Graph Transformer

Vijay Prakash Dwivedi, Sri Jaladi, Yangyi Shen +5

Relational Deep Learning (RDL) is a promising approach for building state-of-the-art predictive models on multi-table relational data by representing it as a heterogeneous temporal…

cs.LG2024★ 3 cited

RelBench: A Benchmark for Deep Learning on Relational Databases

Joshua Robinson, Rishabh Ranjan, Weihua Hu +9

We present RelBench, a public benchmark for solving predictive tasks over relational databases with graph neural networks. RelBench provides databases and tasks spanning diverse do…

cs.LG2024★ 6 cited

From Similarity to Superiority: Channel Clustering for Time Series Forecasting

Jialin Chen, Jan Eric Lenssen, Aosong Feng +5

Time series forecasting has attracted significant attention in recent decades. Previous studies have demonstrated that the Channel-Independent (CI) strategy improves forecasting pe…

cs.LG2024★ 1 cited

PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning

Weihua Hu, Yiwen Yuan, Zecheng Zhang +6

We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal tabular data. PyTorch Frame makes tabular deep learning easy by providing a PyTorch-based dat…