Design Space for Graph Neural Networks (2011.08843v2)

Abstract: The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new architectures as well as novel applications. However, current research focuses on proposing and evaluating specific architectural designs of GNNs, as opposed to studying the more general design space of GNNs that consists of a Cartesian product of different design dimensions, such as the number of layers or the type of the aggregation function. Additionally, GNN designs are often specialized to a single task, yet few efforts have been made to understand how to quickly find the best GNN design for a novel task or a novel dataset. Here we define and systematically study the architectural design space for GNNs which consists of 315,000 different designs over 32 different predictive tasks. Our approach features three key innovations: (1) A general GNN design space; (2) a GNN task space with a similarity metric, so that for a given novel task/dataset, we can quickly identify/transfer the best performing architecture; (3) an efficient and effective design space evaluation method which allows insights to be distilled from a huge number of model-task combinations. Our key results include: (1) A comprehensive set of guidelines for designing well-performing GNNs; (2) while best GNN designs for different tasks vary significantly, the GNN task space allows for transferring the best designs across different tasks; (3) models discovered using our design space achieve state-of-the-art performance. Overall, our work offers a principled and scalable approach to transition from studying individual GNN designs for specific tasks, to systematically studying the GNN design space and the task space. Finally, we release GraphGym, a powerful platform for exploring different GNN designs and tasks. GraphGym features modularized GNN implementation, standardized GNN evaluation, and reproducible and scalable experiment management.

• The paper introduces a systematic framework that explores a vast design space of 315,000 GNN architectures across 32 distinct tasks.
• It employs controlled random search and a Kendall rank correlation metric to evaluate and transfer optimal designs between tasks.
• The study finds that design choices such as batch normalization, PreLU activation, and skip connections consistently boost GNN performance.

Design Space for Graph Neural Networks: A Systematic Framework

The paper, "Design Space for Graph Neural Networks", authored by Jiaxuan You, Rex Ying, and Jure Leskovec, presents a comprehensive exploration of the architectural space pertinent to Graph Neural Networks (GNNs). The paper systematically investigates an expansive design space, comprising 315,000 potential architectures across 32 distinct tasks, to address the limitations of current practices that predominantly focus on specific architectural iterations.

Key Components of the Study

The authors propose three main innovations in their approach:

1. GNN Design Space: This encompasses a general design space constructed through design dimensions such as intra-layer configurations (e.g., activation functions, dropout, batch normalization), inter-layer configurations (e.g., layer stacking, skip connections), and learning configurations (e.g., learning rates, optimizers). By defining this space, the paper emphasizes the significance of evaluating GNNs in a broad context, rather than confining investigations to isolated architectures.
2. Task Space and Transferability: A novel task space is introduced, accompanied by a similarity metric that allows for efficient identification and transfer of optimal architectures across tasks. The utilization of a Kendall rank correlation coefficient provides a quantitative measure to evaluate the transferability of successful designs to new tasks, thus alleviating extensive experimental overheads.
3. Design Space Evaluation: The authors introduce a controlled random search evaluation method, allowing for insightful deductions from an otherwise computationally prohibitive number of model-task combinations. This systematic evaluation further supports the extraction of robust design guidelines across multiple domains.

Findings and Implications

The paper delineates several compelling insights into GNN design:

• Batch Normalization and PreLU: The paper ascertains that the integration of batch normalization and the use of PreLU as an activation function consistently contribute to enhanced performance across various tasks.
• Aggregation Functions: While theoretical literature indicates the expressiveness of sum aggregation, this research substantiates its empirical success across tasks, reaffirming its utility in practical applications.
• Skip Connections: Incorporating skip connections, particularly in the form of skip concatenation, generally augments the efficacy of GNNs, although the optimal configuration of layers remains task-dependent.
• Permutation of Layers: The architectural nuances of layer connectivity and choice of processing layers exhibit significant variation across tasks, reinforcing the need for adaptable GNN designs tailored to specific datasets.

Future Directions

Several avenues for future research emerge from this paper:

• Expanding the Design and Task Spaces: Continued exploration with new intra-layer and inter-layer dimensions, as well as inclusion of diverse and emerging task scenarios, will further consolidate the robustness and applicability of GNN designs.
• Integration with Automated Design Tools: Incorporating these findings into automated machine learning platforms could expedite the adoption of optimal GNN architectures in broader applications.
• Cross-Domain Transferability: Further investigations into cross-domain applicability of GNN designs, utilizing the quantified task similarity metrics, could enhance the generalization capacity and adaptability of GNNs across disparate domains.

Conclusion

This extensive paper lays a foundational framework for systematically exploring GNN design spaces. By providing comprehensive guidelines and a rigorous methodology for evaluating GNN architectures, the research advances the understanding of GNN efficacy across diverse applications. The proposed GraphGym platform complements this framework by facilitating experimentation and reproducibility, thereby promoting a standardized approach to GNN research and application. Through its principled and scalable methodology, the paper offers substantial contributions to both theoretical and practical advancements in the field of graph neural networks.