Long Range Graph Benchmark
Vijay Prakash Dwivedi, Ladislav Ramp\'a\v{s}ek, Mikhail Galkin, Ali, Parviz, Guy Wolf, Anh Tuan Luu, Dominique Beaini

TL;DR
This paper introduces the Long Range Graph Benchmark (LRGB), a collection of datasets designed to evaluate the ability of graph neural network models, including Transformers, to capture long-range interactions crucial for certain tasks.
Contribution
The paper presents LRGB, a new benchmark with datasets that specifically require long-range interaction reasoning, and evaluates GNNs and Transformers on these datasets.
Findings
Models capturing long-range dependencies perform significantly better on LRGB datasets.
LRGB datasets are suitable for benchmarking MP-GNNs and Graph Transformer architectures.
Transformers show improved performance over traditional GNNs on tasks requiring long-range interactions.
Abstract
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…
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Code & Models
- LRGB/voc_superpixels_edge_wt_only_coord_10dataset· 46 dl46 dl
- LRGB/voc_superpixels_edge_wt_only_coord_30dataset· 47 dl47 dl
- LRGB/voc_superpixels_edge_wt_coord_feat_10dataset· 59 dl59 dl
- LRGB/voc_superpixels_edge_wt_coord_feat_30dataset· 67 dl67 dl
- LRGB/voc_superpixels_edge_wt_region_boundary_10dataset· 10 dl10 dl
Videos
Taxonomy
TopicsAdvanced Graph Neural Networks · Machine Learning in Materials Science · Bioinformatics and Genomic Networks
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Laplacian EigenMap · Label Smoothing · Absolute Position Encodings · Softmax · Adam · Laplacian Positional Encodings · Position-Wise Feed-Forward Layer
