How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision (2204.04879v1)

Abstract: Attention mechanism in graph neural networks is designed to assign larger weights to important neighbor nodes for better representation. However, what graph attention learns is not understood well, particularly when graphs are noisy. In this paper, we propose a self-supervised graph attention network (SuperGAT), an improved graph attention model for noisy graphs. Specifically, we exploit two attention forms compatible with a self-supervised task to predict edges, whose presence and absence contain the inherent information about the importance of the relationships between nodes. By encoding edges, SuperGAT learns more expressive attention in distinguishing mislinked neighbors. We find two graph characteristics influence the effectiveness of attention forms and self-supervision: homophily and average degree. Thus, our recipe provides guidance on which attention design to use when those two graph characteristics are known. Our experiment on 17 real-world datasets demonstrates that our recipe generalizes across 15 datasets of them, and our models designed by recipe show improved performance over baselines.

Overview of "How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision"

The paper introduces SuperGAT, a self-supervised graph attention network designed to improve the learning capabilities of conventional graph attention networks (GATs) when applied to noisy graphs. Unlike traditional GATs, which can struggle with inaccurate or irrelevant connections, SuperGAT incorporates a self-supervised mechanism to better discern the importance of relationships between nodes based on edge presence.

Key Contributions

1. Self-Supervised Graph Attention Mechanism: SuperGAT leverages self-supervision tasks to predict edge presence, guiding the attention mechanism towards more predictive and discriminant edges. This approach contrasts with traditional GATs, which lack explicit supervision for attention values.
2. Evaluation of Attention Forms: The paper analyzes two attention mechanisms—GAT's original single-layer neural network (GO) and the dot-product (DP) attention. These mechanisms were evaluated for their effectiveness in two tasks: link prediction and capturing label agreement. The paper found that DP attention better predicts edge presence, while GO attention is more aligned with capturing label agreement.
3. Graph Characteristics Influence on Attention Design: The paper identified homophily and average degree as crucial graph characteristics that influence the effectiveness of attention mechanisms. SuperGAT exploits these characteristics to decide which attention design—scaled dot-product (SD) or mixed attention (MX)—is optimal for a particular graph.
4. Empirical Validation: Empirical tests across 17 real-world datasets demonstrate that models designed according to the provided guidelines generalize well, outperforming standard baselines in the majority of cases.

Experimental Findings

• Node Classification and Link Prediction Performance: Experiments revealed a trade-off between node classification accuracy and link prediction capability, with a particular emphasis on the choice of the self-supervision coefficient. The results highlighted the necessity of balancing these tasks to maximize the performance of SuperGAT.
• Synthetic Graphs Analysis: Controlled experiments on synthetic datasets emphasized the variability in performance based on average degree and homophily. These synthetic studies provided a detailed understanding of graph attention forms' adaptability, which was then validated on real-world datasets.
• Overarching Performance: In comparison to baseline models like GCN, GraphSAGE, and standard GAT, SuperGAT demonstrated superior performance particularly in settings with specific graph characteristics, confirming the proposed methodology's effectiveness.

Practical and Theoretical Implications

Practically, SuperGAT can be used to improve the reliability and expressiveness of GNNs in applications characterized by noise, such as social networks and biological systems. Theoretically, the proposed approach enriches the understanding of how graph structure can be leveraged using self-supervised learning to refine attention mechanisms. The insights regarding the link between graph properties and attention design will aid future research in designing GNNs that are more robust in varied network conditions.

Future Directions

Future research could explore more complex self-supervised tasks that might further enhance the learning capacity of graph-based models. Additionally, investigating the integration of SuperGAT with different neural architectures could lead to broader applications, enhancing supervised learning with various downstream tasks. Overall, this paper lays a substantial foundation for developing more adaptive graph neural networks through strategic self-supervision.