Auto-GNN: Neural Architecture Search of Graph Neural Networks (1909.03184v2)

Abstract: Graph neural networks (GNN) has been successfully applied to operate on the graph-structured data. Given a specific scenario, rich human expertise and tremendous laborious trials are usually required to identify a suitable GNN architecture. It is because the performance of a GNN architecture is significantly affected by the choice of graph convolution components, such as aggregate function and hidden dimension. Neural architecture search (NAS) has shown its potential in discovering effective deep architectures for learning tasks in image and LLMing. However, existing NAS algorithms cannot be directly applied to the GNN search problem. First, the search space of GNN is different from the ones in existing NAS work. Second, the representation learning capacity of GNN architecture changes obviously with slight architecture modifications. It affects the search efficiency of traditional search methods. Third, widely used techniques in NAS such as parameter sharing might become unstable in GNN. To bridge the gap, we propose the automated graph neural networks (AGNN) framework, which aims to find an optimal GNN architecture within a predefined search space. A reinforcement learning based controller is designed to greedily validate architectures via small steps. AGNN has a novel parameter sharing strategy that enables homogeneous architectures to share parameters, based on a carefully-designed homogeneity definition. Experiments on real-world benchmark datasets demonstrate that the GNN architecture identified by AGNN achieves the best performance, comparing with existing handcrafted models and tradistional search methods.

• The paper presents Auto-GNN, a framework using Reinforced Conservative Neural Architecture Search (RCNAS) to automate the design of Graph Neural Network (GNN) architectures.
• Auto-GNN defines a specific search space for GNN components and employs constrained parameter sharing for efficient and stable architecture discovery.
• Experimental results show Auto-GNN outperforms handcrafted GNNs and existing NAS methods on benchmark datasets for node classification.

Analysis of "Auto-GNN: Neural Architecture Search of Graph Neural Networks"

The paper "Auto-GNN: Neural Architecture Search of Graph Neural Networks" presents an automated framework for discovering optimal Graph Neural Network (GNN) architectures tailored for various node classification tasks. The authors highlight the limitations of current manual efforts in designing effective GNN architectures, emphasizing the necessity for automated solutions to navigate the expansive design space efficiently and effectively.

Key Contributions and Methodology

The core contribution of this paper is the development of the Automated Graph Neural Networks (AGNN) framework utilizing Neural Architecture Search (NAS). Traditional NAS methods have showcased their utility in image and language tasks; however, their application to GNNs remains constrained due to several unique challenges. These are primarily due to the distinct graph-based search space, the sensitivity of GNNs to architectural tweaks, and the instability of parameter-sharing popular in NAS.

1. Search Space Definition: The authors meticulously define a search space that encompasses vital components of GNN layers, such as hidden dimension, attention mechanisms, aggregate functions, and activation functions. Each layer’s configuration is expressed as a combination of these components, and the overall architecture is a concatenation of multiple such layers.
2. Controller Design: The design introduces a Reinforced Conservative Neural Architecture Search (RCNAS) controller, which deviates from traditional NAS controllers by conserving the best-found architecture and only performing slight modifications to specific components. This approach is aimed at accelerating the discovery of performant architectures by learning which components are most impactful with minimal computational overhead.
3. Constrained Parameter Sharing: Unlike typical NAS approaches, AGNN employs a constrained parameter-sharing mechanism that accounts for architecture homogeneity. The homogeneity is defined through identical tensor shapes and selected functions within layers, ensuring stable training processes when parameters are shared.

Experimental Evaluation

The experiments conducted validate the efficacy of AGNN across several benchmarks—Cora, Citeseer, Pubmed, and PPI datasets—spanning transductive and inductive learning settings. The results show that AGNN consistently outperforms handcrafted GNN architectures such as Chebyshev, GCN, and GraphSAGE, as well as existing NAS approaches. Notably, AGNN achieves superior classification accuracy and F1 scores, showcasing its capability to automate GNN design without compromising performance.

Moreover, the paper discusses the trade-offs between computation cost and model performance, where parameter sharing brings down training time substantially albeit at a slight performance cost. In scenarios with adequate computational resources, training architectures from scratch yields the best results.

Implications and Future Directions

The implications of this research are manifold. Practically, AGNN represents a significant step toward democratizing GNN architecture design, removing the bottleneck of manual trial-and-error processes. Theoretically, it paves the way for advancing NAS methodologies tailored to graph domains, highlighting the need for novel strategies that account for graph-specific characteristics.

Future work could extend AGNN towards other applications such as link prediction and graph classification, potentially integrating more advanced convolutional techniques. Additionally, researchers may explore further optimization within the AGNN framework, such as incorporating dynamic search spaces or hybrid models combining different learning paradigms.

Overall, "Auto-GNN: Neural Architecture Search of Graph Neural Networks" contributes valuable insights and methodologies that could catalyze further advancements in automated machine learning, particularly in graph-based data contexts.