OpenGSL: A Comprehensive Benchmark for Graph Structure Learning (2306.10280v4)

Abstract: Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node attributes. However, the inherent suboptimal nature of node connections, resulting from the complex and contingent formation process of graphs, presents significant challenges in modeling them effectively. To tackle this issue, Graph Structure Learning (GSL), a family of data-centric learning approaches, has garnered substantial attention in recent years. The core concept behind GSL is to jointly optimize the graph structure and the corresponding GNN models. Despite the proposal of numerous GSL methods, the progress in this field remains unclear due to inconsistent experimental protocols, including variations in datasets, data processing techniques, and splitting strategies. In this paper, we introduce OpenGSL, the first comprehensive benchmark for GSL, aimed at addressing this gap. OpenGSL enables a fair comparison among state-of-the-art GSL methods by evaluating them across various popular datasets using uniform data processing and splitting strategies. Through extensive experiments, we observe that existing GSL methods do not consistently outperform vanilla GNN counterparts. We also find that there is no significant correlation between the homophily of the learned structure and task performance, challenging the common belief. Moreover, we observe that the learned graph structure demonstrates a strong generalization ability across different GNN models, despite the high computational and space consumption. We hope that our open-sourced library will facilitate rapid and equitable evaluation and inspire further innovative research in this field. The code of the benchmark can be found in https://github.com/OpenGSL/OpenGSL.

A Critical Overview of OpenGSL: A Comprehensive Benchmark for Graph Structure Learning

Graph Neural Networks (GNNs) have become an integral tool for processing graph-structured data, principally due to their proficiency in amalgamating graph topology with node attributes. Despite their impressive performance, the suboptimal connections inherent in real-world graphs pose substantial modeling challenges. Graph Structure Learning (GSL) emerges as a promising solution, focusing on optimally reconstructing graph structures to augment GNN performance. However, progress in GSL remains ambiguous due to disparate experimental setups, making a standardized evaluation imperative.

The paper "OpenGSL: A Comprehensive Benchmark for Graph Structure Learning" by Zhou et al. introduces the first unified benchmark for GSL—OpenGSL. This benchmark ensures a fair comparative analysis of state-of-the-art (SOTA) GSL methods by applying consistent data processing and splitting across various datasets. The paper encompasses 13 GSL methods, evaluated on 10 distinct datasets, providing insights into the efficacy and challenges of existing approaches.

Key Contributions

1. Benchmark Implementation: OpenGSL facilitates unbiased performance comparisons by harmonizing experimental settings across methods and datasets. Notably, the results reveal that GSL methods struggle to consistently outperform vanilla GNNs like GCN. This highlights a discrepancy between theoretical advancements and real-world applicability, especially on heterophilous graphs.
2. Multi-dimensional Analysis: The paper systematically examines the homophily of learned structures, their generalizability across different GNN models, and computational efficiency. Interestingly, no significant correlation was found between structural homophily and task performance, challenging conventional assumptions about homophily's role in GSL. Furthermore, while GSL enhances structure generalization, most methods exhibit significant time and memory inefficiencies.
3. Open-source Library: The authors have publicly released the benchmark library, encouraging further exploration and method development. This transparency is crucial for fostering innovation in addressing the gaps identified by the paper.

Implications and Future Directions

The insights drawn from OpenGSL have several practical and theoretical implications:

• Reevaluation of Homophily: The lack of correlation between homophily and performance necessitates a reevaluation of current GSL objectives. Future research should explore alternative graph structural properties that impact learning efficacy.
• Development of Adaptive GSL Methods: The observed heterogeneity in GSL effectiveness across different datasets highlights the need for adaptive methods. Research should aim at methods that dynamically adjust to the graph's intrinsic properties, potentially leveraging advancements in adaptive learning.
• Efficiency Enhancement: Addressing computational inefficiencies is crucial for the practical deployment of GSL methods, particularly on large-scale graphs. Innovative approaches, possibly incorporating sampling techniques or efficient approximation algorithms, could mitigate these concerns.
• Task-agnostic GSL: Expanding GSL's application beyond node classification to encompass a diverse range of graph-related tasks could significantly broaden the scope and impact of this field. Future exploration into task-agnostic methods could provide robust solutions across varying graph analysis requirements.

In summary, OpenGSL represents a pivotal step toward standardizing GSL evaluation, laying the groundwork for targeted advancements in the domain. By pinpointing existing method limitations and proposing future directives, Zhou et al. catalyze the rigorous investigation necessary for GSL to achieve its full potential in enhancing graph representation learning.