Long Range Graph Benchmark

By Vijay Prakash Dwivedi, Ladislav Rampášek, Mikhail Galkin, Alipanah Parviz, Guy Wolf, Anh Tuan Luu, Dominique Beaini. Neural Information Processing Systems, 2022

345 citations recorded by the source corpus.

Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer. In principle, such networks are not able to capture long-range interactions (LRI) that may be desired or necessary for learning a given task on graphs. Recently, there has been an increasing interest in development of Transformer-based methods for graphs that can consider full node connectivity beyond the original sparse structure, thus enabling the modeling of LRI. However, MP-GNNs that simply rely on 1-hop message passing often fare better in several existing graph benchmarks when combined with positional feature representations, among other innovations, hence limiting the perceived utility and ranking of Transformer-like architectures. Here, we present the Long Range Graph Benchmark (LRGB) with 5 graph learning datasets: PascalVOC-SP, COCO-SP, PCQM-Contact, Peptides-func and Peptides-struct that arguably require LRI reasoning to achieve strong performance in a given task. We benchmark both baseline GNNs and Graph Transformer networks to verify that the models which capture long-range dependencies perform significantly better on these tasks. Therefore, these datasets are suitable for benchmarking and exploration of MP-GNNs and Graph Transformer architectures that are intended to capture LRI.

Subfields: Artificial Intelligence, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f7a3d9bcf052f2b4ef7d59dcca4013ea11081d0f

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In Artificial Intelligence

1. Jiliang Tang
2. Dawei Yin
3. Hui Liu
4. Wenqi Fan
5. Wei Jin
6. Shuaiqiang Wang
7. Hang Li
8. Jun Xu

In Materials Chemistry

1. Jiliang Tang
2. Hang Li

In Molecular Biology

1. Wei Jin
2. Jiliang Tang
3. Wenqi Fan
4. Hang Li
5. Dawei Yin

Citations in the corpus

• Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs (2023, methodology): “Following our proposed two pipelines, i.e., LLMs as the En-hancers and LLMs as the Predictors, we review existing works in this section.”