Long Range Graph Benchmark (2206.08164v4)

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 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.

An In-depth Analysis of the Long Range Graph Benchmark (LRGB)

The paper "Long Range Graph Benchmark" (LRGB) presents a set of graph datasets designed to evaluate the capacity of Graph Neural Networks (GNNs) and Graph Transformer models in capturing long-range interactions (LRI). This work addresses the limitations of existing graph datasets that predominantly emphasize local graph structures, thereby obstructing the fair assessment of models capable of exploiting LRI. To remedy this, five novel benchmarks—PascalVOC-SP, COCO-SP, PCQM-Contact, Peptides-func, and Peptides-struct—are proposed, targeting various domains such as computer vision and quantum chemistry.

Overview of Key Contributions

The paper introduces the LRGB framework to offer datasets that genuinely challenge GNNs on tasks that necessitate long-range signal propagation. The authors systematically analyze the characteristic factors that qualify a dataset as requiring LRI:

1. Graph Size: Larger graphs provide a platform where local message-passing GNNs are challenged by extensive receptive fields, leading to information oversquashing.
2. Nature of Task: Tasks that inherently depend on aggregates of non-local information necessitate architectures capable of navigating these dependencies.
3. Global Graph Structure Contribution: Datasets where tasks benefit substantially from global structural information further underscore the need for models that can capture LRIs.

The paper's empirical investigations reveal that models equipped with Transformer-like mechanisms excel on these long-range tasks, compared to traditional local MP-GNNs.

Analysis of the Long Range Graph Benchmark Datasets

1. PascalVOC-SP and COCO-SP:
   • Derived from image datasets, these benchmarks consist of nodes representing superpixels, demanding semantic segmentation on large graphs. The large graph sizes make them apt candidates for evaluating LRIs.
2. PCQM-Contact:
   • This dataset imposes the complex task of predicting 3D spatial proximity between non-adjacent nodes in molecular graphs. The inherent need for understanding molecular structure highlights the necessity for LRI modeling.
3. Peptides-func and Peptides-struct:
   • Utilizing peptide graphs, these tasks require understanding both the function and the 3D structure of peptides. Their diverse graph diameters and the absence of explicit 3D structural information accentuate the importance of effective LRI strategies.

Empirical Findings

The paper's experiments involve comparing multiple MP-GNNs with Graph Transformer models. Notably, attention-based Graph Transformers generally outperform MP-GNN counterparts in terms of capturing LRIs in the proposed benchmarks. Surprisingly, positional and structural encodings, which are vital for global structural awareness, show limited impact in enhancing MP-GNN performance, suggesting an avenue for exploring more sophisticated encoding strategies.

Implications and Future Directions

The LRGB datasets provide a challenging testbed encouraging the design of GNN and Transformer models focusing on LRI. These datasets expose the shortcoming of existing architectures and provide a rich field for exploring augmentation mechanisms such as positional encodings.

As the community ventures into optimizing Graph Transformers for efficiency and scalability, particularly in handling large datasets, LRGB holds promise for benchmark testing the effectiveness of emerging models in the field of LRI. Further, research could investigate methods to seamlessly integrate local and global graph information in a manner that mitigates information bottlenecks without the computational overhead of full-graph attention.

In summary, this paper introduces a series of thoughtfully crafted benchmarks that fill a vital gap in the evaluation of models designed to capture long-range dependencies, catalyzing future innovations in enhancing the capacity of graph-learning techniques.