GSLB: The Graph Structure Learning Benchmark (2310.05174v1)

Abstract: Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://github.com/GSL-Benchmark/GSLB.

• The paper introduces a comprehensive benchmark that unifies 16 GSL algorithms across 20 diverse datasets to standardize performance evaluation.
• The study assesses effectiveness, robustness, and complexity, uncovering improved node classification in heterophilic graphs and strong noise resilience.
• The paper highlights scalability challenges due to high computational costs, emphasizing the need for more efficient and scalable GSL architectures.

An Academic Overview of "GSLB: The Graph Structure Learning Benchmark"

The paper "GSLB: The Graph Structure Learning Benchmark" addresses a need in the graph structure learning (GSL) community for standardized benchmarks to evaluate and compare GSL methods effectively. As the field has progressed rapidly, with numerous techniques being proposed, a lack of coherence in the experimental setups has hindered a holistic understanding of advancements. This paper introduces a comprehensive benchmark framework, GSLB, which unifies 16 state-of-the-art GSL algorithms across varied tasks and datasets to cultivate a more structured evaluation landscape.

Framework and Methodology

The GSLB benchmark is composed of 20 diverse datasets and focuses on graph neural networks (GNNs) that optimize both model parameters and graph structures. The benchmark delineates evaluation along three critical dimensions: effectiveness, robustness, and complexity. Specifically, it deals with:

• Effectiveness: Evaluated across node-level classification (both homogeneous and heterogeneous) and graph-level tasks. The datasets cover a wide range from heavily homophilic to heterophilic graph characteristics.
• Robustness: Assessed under varying noise conditions in supervision signals, structure, and features. The benchmark provides insights into how these models can adapt under adverse conditions.
• Complexity: Explores both time and space complexity to evaluate the scalability of these methods, particularly on larger datasets such as ogbn-arxiv.

Findings and Contributions

Through extensive experiments, the paper offers several key insights:

1. Node- and Graph-Level Tasks: GSL methods typically enhance performance in node classification tasks, especially in heterophilic graphs where traditional GNNs struggle due to challenges in message passing assumptions. In graph-level tasks, the benefits of GSL are less pronounced with performance variability across datasets.
2. Robustness: GSL methods demonstrate resilience against various types of noise, suggesting their potential in unreliable settings. Unsupervised GSL approaches like STABLE and SUBLIME show impressive robustness, hinting at the advantage of self-supervised techniques in refining graph structures.
3. Scalability Challenges: Most GSL methods face issues in scaling, primarily due to high computational demands that restrict their application to large-scale datasets. The analysis in terms of time and memory complexity highlights the need for more efficient architectures.

The paper's introduction of GSLB marks a significant step towards unified evaluation, facilitating reproducibility and comparability in GSL research. By publishing the benchmark along with an accessible library, the authors aim to bridge gaps in current methodologies and promote further exploration in efficient and robust GSL models.

Implications and Future Directions

The outcomes of this work provide a foundation for future investigations into scalable GSL approaches and the refinement of heterogeneous and dynamic graphs. Subsequent research could focus on addressing scalability issues by reducing operational complexity or employing alternative learning paradigms. Furthermore, the interesting observation of robust performance with few labels opens avenues to explore graph learning in low-supervision environments.

An understated area of growth is in unsupervised GSL, which showed resistance to both structural and feature changes, suggesting its applicability in defense tasks against adversarial attacks. Continued research in this area could significantly enhance the robustness of GNNs in volatile real-world applications.

Overall, GSLB aims to establish a baseline for future GSL studies, promoting standardized practices that could drive the development of more resilient, scalable, and efficient graph learning frameworks.