UGSL: A Unified Framework for Benchmarking Graph Structure Learning (2308.10737v1)

Abstract: Graph neural networks (GNNs) demonstrate outstanding performance in a broad range of applications. While the majority of GNN applications assume that a graph structure is given, some recent methods substantially expanded the applicability of GNNs by showing that they may be effective even when no graph structure is explicitly provided. The GNN parameters and a graph structure are jointly learned. Previous studies adopt different experimentation setups, making it difficult to compare their merits. In this paper, we propose a benchmarking strategy for graph structure learning using a unified framework. Our framework, called Unified Graph Structure Learning (UGSL), reformulates existing models into a single model. We implement a wide range of existing models in our framework and conduct extensive analyses of the effectiveness of different components in the framework. Our results provide a clear and concise understanding of the different methods in this area as well as their strengths and weaknesses. The benchmark code is available at https://github.com/google-research/google-research/tree/master/ugsl.

• The paper introduces UGSL as a unified framework that consolidates ten GSL models for systematic benchmarking.
• It demonstrates the effectiveness of components like FP edge scorers and d-kNN sparsifiers in enhancing graph representation.
• Extensive experiments reveal that no universal architecture exists, underscoring the need for task-specific model selection.

UGSL: A Unified Framework for Benchmarking Graph Structure Learning

The paper "UGSL: A Unified Framework for Benchmarking Graph Structure Learning" offers a comprehensive examination of Graph Structure Learning (GSL), a field that focuses on optimizing graph structures to enhance graph representation learning tasks. The researchers propose a unified framework, UGSL, which consolidates various existing GSL models into a single architecture with the objective of allowing for systematic benchmarking and analysis.

Overview

Graph representation learning (GRL) has become crucial across multiple domains where data naturally forms graphs. The performance of graph neural networks (GNNs), a dominant approach within GRL, is often hampered by the quality of given graph structures. This inadequacy of input graphs has led to the emergence of GSL, which aims to construct optimal graph structures when they are noisy, incomplete, or unavailable.

The UGSL framework seeks to address inconsistencies in experimentation setups across current studies by providing a unified structure for the evaluation of graph structure learning models. It reformulates ten existing models and allows comparative analysis through extensive experimentation.

Contributions

The UGSL framework encompasses a wide range of existing models and supports over four thousand architectures, creating a robust benchmarking environment. The framework includes:

1. Edge Scorers: Utilizes models like Multi-Layer Perceptron (MLP), Attention Mechanisms (ATT), and Full Parameterization (FP) for determining edge significance.
2. Sparsifiers: Employs techniques such as k-nearest neighbors (kNN), dilated-kNN (d-kNN), and epsilon-nearest neighbors (εNN) to manage graph sparsity.
3. Processors: Implements modules to manage symmetry and apply nonlinear transformations to processed graphs.
4. Encoders: Integrates GNN models like GCN and GIN, providing mechanisms to encode node features effectively.

The paper reports on an exhaustive set of experiments conducted across six different datasets, revealing insightful dependencies between architectural choices and task performance.

Key Findings

The findings emphasize the importance of selecting appropriate components based on the specific characteristics of datasets. The experiments highlight:

• The effectiveness of the FP edge scorer across multiple datasets compared to MLP and ATT, except in scenarios with expressive features.
• The utility of the d-kNN sparsifier, which consistently outperformed other sparsification methods.
• The significant benefits derived from combining unsupervised loss functions with regularizers, particularly with denoising and contrastive losses.

Additionally, the results obtained from extensive random searches indicate no universally optimal architecture, suggesting that component selection should be task-specific and dataset-dependent.

Implications and Future Directions

The UGSL framework provides a significant contribution to the experimental consistency and comparability in the GSL field. By enabling thorough benchmarking, it aids in understanding the strengths and weaknesses of various components in GSL models.

The work hints at future exploration avenues, such as scalability improvements for larger datasets and diversification of application tasks beyond node classification, like link prediction and graph classification. There is also potential for developing new graph-level statistics to better correlate graph structures with GNN downstream performance.

In conclusion, UGSL serves as a pioneering framework for comprehensively evaluating graph structure learning methodologies, fostering a deeper understanding of their applicability and informing future research in diverse AI domains.