NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search

Yijian Qin, Ziwei Zhang, Xin Wang, Zeyang Zhang, Wenwu Zhu

Keywords: Graph neural architecture search, neural architecture search benchmark

Abstract: Graph neural architecture search (GraphNAS) has recently aroused considerable attention in both academia and industry. However, two key challenges seriously hinder the further research of GraphNAS. First, since there is no consensus for the experimental setting, the empirical results in different research papers are often not comparable and even not reproducible, leading to unfair comparisons. Secondly, GraphNAS often needs extensive computations, which makes it highly inefficient and inaccessible to researchers without access to large-scale computation. To solve these challenges, we propose NAS-Bench-Graph, a tailored benchmark that supports unified, reproducible, and efficient evaluations for GraphNAS. Specifically, we construct a unified, expressive yet compact search space, covering 26,206 unique graph neural network (GNN) architectures and propose a principled evaluation protocol. To avoid unnecessary repetitive training, we have trained and evaluated all of these architectures on nine representative graph datasets, recording detailed metrics including train, validation, and test performance in each epoch, the latency, the number of parameters, etc. Based on our proposed benchmark, the performance of GNN architectures can be directly obtained by a look-up table without any further computation, which enables fair, fully reproducible, and efficient comparisons. To demonstrate its usage, we make in-depth analyses of our proposed NAS-Bench-Graph, revealing several interesting findings for GraphNAS. We also showcase how the benchmark can be easily compatible with GraphNAS open libraries such as AutoGL and NNI. To the best of our knowledge, our work is the first benchmark for graph neural architecture search.

Author Statement: Yes

URL: https://github.com/THUMNLab/NAS-Bench-Graph

License: code: Apache License 2.0

TL;DR: First tabular NAS benchmark for graph learning.

Contribution Process Agreement: Yes

In Person Attendance: Yes

Community Implementations: [ 5 code implementations](https://www.catalyzex.com/paper/nas-bench-graph-benchmarking-graph-neural/code)