Probabilistic Dual Network Architecture Search on Graphs
Yiren Zhao, Duo Wang, Xitong Gao, Robert Mullins, Pietro Lio, Mateja, Jamnik

TL;DR
This paper introduces PDNAS, a differentiable NAS method for GNNs that optimizes both micro- and macro-architectures, leading to better performance across datasets.
Contribution
It presents the first fully gradient-based differentiable NAS framework for GNNs, optimizing both intra-block operations and inter-block connections.
Findings
PDNAS outperforms hand-designed GNNs and other NAS methods.
On the PPI dataset, PDNAS exceeds competitors by 1.67 and 0.17 in F1 scores.
It enables deeper GNN architectures with improved adaptability.
Abstract
We present the first differentiable Network Architecture Search (NAS) for Graph Neural Networks (GNNs). GNNs show promising performance on a wide range of tasks, but require a large amount of architecture engineering. First, graphs are inherently a non-Euclidean and sophisticated data structure, leading to poor adaptivity of GNN architectures across different datasets. Second, a typical graph block contains numerous different components, such as aggregation and attention, generating a large combinatorial search space. To counter these problems, we propose a Probabilistic Dual Network Architecture Search (PDNAS) framework for GNNs. PDNAS not only optimises the operations within a single graph block (micro-architecture), but also considers how these blocks should be connected to each other (macro-architecture). The dual architecture (micro- and marco-architectures) optimisation allows…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Advanced Neural Network Applications · Graph Theory and Algorithms
