AutoCoG: A Unified Data-Model Co-Search Framework for Graph Neural NetworksDownload PDF

25 Feb 2022, 12:35 (modified: 16 Jul 2022, 13:35)AutoML-Conf 2022 (Main Track)Readers: Everyone
Abstract: Neural architecture search (NAS) has demonstrated success in discovering promising architectures for vision or language modeling tasks, and it has recently been introduced to searching for graph neural networks (GNNs) as well. Despite the preliminary success, GNNs struggle in dealing with heterophily or low-homophily graphs where connected nodes may have different class labels and dissimilar features. To this end, we propose co-optimizing both the input graph topology and the model's architecture topology simultaneously. That yields AutoCoG, the first unified data-model co-search NAS framework for GNNs. By defining a highly flexible data-model co-search space, AutoCoG is gracefully formulated as a principled bi-level optimization that can be end-to-end solved by the differentiable search methods. Experiments show AutoCoG achieves gains of up to 4% for Actor, 7.3% on average for Web datasets, 0.17% for CoAuthor-CS, and finally 5.4% for Wikipedia-Photo benchmarks. All codes will be released upon paper acceptance.
Keywords: GCN, NAS
One-sentence Summary: We propose AutoCoG, a NAS framework to unified model's architecture search and graph-augmentation in a differentiable manner.
Track: Main track
Reproducibility Checklist: Yes
Broader Impact Statement: Yes
Paper Availability And License: Yes
Code Of Conduct: Yes
Reviewers: duc hoang,
Main Paper And Supplementary Material: pdf
CPU Hours: 0
GPU Hours: 48
TPU Hours: 0
Evaluation Metrics: No
Estimated CO2e Footprint: 5.18
Class Of Approaches: Gradient-based Methods, Progressive Search
Datasets And Benchmarks: WebKB-Texas, WebKB-Wisconsin, WebKB-Cornell, CoAuthor-CS, CoAuthor-Physics, Amazon-Photos, Amazon-Computers, Actor
Performance Metrics: Accuracy
Benchmark Performance: Actor 38.04 Texas 80.27 Wisconsin 80.39 Cornell 64.86 Computer 78.91 CS 92.05 Photos 85.16 Physics 93.28
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