Graph Information Bottleneck (2010.12811v1)

Abstract: Representation learning of graph-structured data is challenging because both graph structure and node features carry important information. Graph Neural Networks (GNNs) provide an expressive way to fuse information from network structure and node features. However, GNNs are prone to adversarial attacks. Here we introduce Graph Information Bottleneck (GIB), an information-theoretic principle that optimally balances expressiveness and robustness of the learned representation of graph-structured data. Inheriting from the general Information Bottleneck (IB), GIB aims to learn the minimal sufficient representation for a given task by maximizing the mutual information between the representation and the target, and simultaneously constraining the mutual information between the representation and the input data. Different from the general IB, GIB regularizes the structural as well as the feature information. We design two sampling algorithms for structural regularization and instantiate the GIB principle with two new models: GIB-Cat and GIB-Bern, and demonstrate the benefits by evaluating the resilience to adversarial attacks. We show that our proposed models are more robust than state-of-the-art graph defense models. GIB-based models empirically achieve up to 31% improvement with adversarial perturbation of the graph structure as well as node features.

Evaluation of the Graph Information Bottleneck (GIB) Approach for Robust Representation Learning on Graph-Structured Data

The research delineated in "Graph Information Bottleneck" by Wu et al. presents a novel approach aimed at enhancing the robustness and expressiveness of representations learned from graph-structured data. The paper introduces a framework known as the Graph Information Bottleneck (GIB), which is firmly rooted in information-theoretic principles, thereby extending the general Information Bottleneck (IB) framework to accommodate the unique challenges posed by graph-structured datasets.

Theoretical Foundations and Methodology

GIB builds upon the foundational concept of IB, which posits that optimal data representations should encapsulate the minimal yet sufficient information required for a given task. The authors adeptly adapt this notion to graph data by proposing a dual-focus on regularizing both the structural and feature information inherent in graph nodes. This is a significant departure from traditional IB models that typically assume independent and identically distributed (i.i.d.) data.

The GIB framework is operationalized through the introduction of two novel models: GIB-Cat and GIB-Bern. These models instantiate GIB by employing sampling algorithms for structural regularization, leveraging respectively, categorical and Bernoulli distributions. The novel approach incorporates variational bounds for tractability, utilizing a dual bound strategy — a variational upper bound for constraining feature and structural information and a variational lower bound for maximizing task-relevant information.

Empirical Evaluation

Robustness in representation learning is evaluated by subjecting GIB-based models to adversarial attacks, a known vulnerability in Graph Neural Networks (GNNs). The proposed GIB-Cat and GIB-Bern models demonstrate substantial resilience, achieving up to a 31% improvement in accuracy under adversarial conditions targeting both graph structures and node features. Comparatively, these models outperform existing defense mechanisms such as GCNJaccard and Robust GCN (RGCN), which are specially tailored to mitigate adversarial interventions.

Key Contributions and Implications

• Information-Theoretic Generalization: The GIB framework marks a significant advancement in extending information-theoretic models to non-i.i.d. settings characteristic of graph-structured data. It underscores the dual necessity of capturing minimal information from node features and graph structures.
• Adversarial Robustness: Through empirical comparisons, the paper illustrates the marked improvement in model robustness against structural and feature-targeted adversarial attacks, suggesting practical applications in areas where data integrity is paramount.
• Scalable Algorithms and Pragmatic Bounds: GIB’s reliance on variational bounds not only ensures scalability but also enriches the understanding of mutual information in graph-based representations.

Future Directions

This research provides a scientific basis that could inform several future endeavors:

1. Alternative Instantiations: The exploration of additional architectures that can implement the GIB principle is likely to yield diverse applications across graph-related tasks.
2. Relaxation of Local Dependence: Investigating approaches that relax the local dependence assumption might improve the scope and applicability of GIB in larger-scale graphs with intricate structures.
3. Diverse Graph Tasks: Extending GIB to tasks beyond node classification, such as link prediction and graph classification, represents a promising direction for future exploration.

In conclusion, the GIB framework presented by Wu et al. is robust in its theoretical underpinnings and impactful in practical applications, offering substantial improvements in the domain of graph representation learning under adversarial conditions. Its development marks an important progression in the application of IB principles to the intricate domain of graph-structured data, opening avenues for further research and application in real-world scenarios.