Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data (2108.01099v2)

Abstract: There has been a recent surge of interest in designing Graph Neural Networks (GNNs) for semi-supervised learning tasks. Unfortunately this work has assumed that the nodes labeled for use in training were selected uniformly at random (i.e. are an IID sample). However in many real world scenarios gathering labels for graph nodes is both expensive and inherently biased -- so this assumption can not be met. GNNs can suffer poor generalization when this occurs, by overfitting to superfluous regularities present in the training data. In this work we present a method, Shift-Robust GNN (SR-GNN), designed to account for distributional differences between biased training data and the graph's true inference distribution. SR-GNN adapts GNN models for the presence of distributional shifts between the nodes which have had labels provided for training and the rest of the dataset. We illustrate the effectiveness of SR-GNN in a variety of experiments with biased training datasets on common GNN benchmark datasets for semi-supervised learning, where we see that SR-GNN outperforms other GNN baselines by accuracy, eliminating at least (~40%) of the negative effects introduced by biased training data. On the largest dataset we consider, ogb-arxiv, we observe an 2% absolute improvement over the baseline and reduce 30% of the negative effects.

• The paper introduces SR-GNN, a novel framework employing distributional regularization and instance reweighting to counteract biases from non-IID training data.
• It demonstrates significant improvements by reclaiming up to 40% of lost accuracy on biased benchmarks and achieving a 2% boost on large-scale datasets.
• The methodology enhances real-world applicability in areas like fraud detection and social network analysis while opening avenues for domain adaptation research.

Shift-Robust GNNs: Addressing Localized Bias in Graph Neural Network Training

The paper "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" investigates the challenge of training Graph Neural Networks (GNNs) with biased labeled data and proposes a novel framework, the Shift-Robust GNN (SR-GNN), to improve GNN performance under distributional shift conditions. This work addresses a significant gap in GNN research, where most existing models assume that training data is independently and identically distributed (IID), which is often unrealistic in real-world applications.

Problem Statement

GNNs have gained traction for semi-supervised learning tasks on graphs, effectively leveraging both node features and graph topology. However, label acquisition for graph nodes is typically expensive and may be prone to bias, leading to distributional differences between the training data and the true distribution of the graph. This misalignment introduces a problem where a GNN might overfit to spurious patterns in the biased training data, resulting in poor generalization performance.

Methodology

SR-GNN is introduced as a framework to mitigate the impact of such distributional shifts, applicable to both typical deep GNNs and newer linearized GNN variants. The key components of SR-GNN involve:

• Distributional Regularization: Adding a regularization term to the loss function to minimize discrepancies between the training and test data representations.
• Instance Reweighting: Adjusting the weights of training instances to compensate for their deviation from an IID sample, employing techniques such as Kernel Mean Matching (KMM) for effective adaptation.

The framework's adaptability extends to both traditional GNNs, like Graph Convolutional Networks (GCN), where the entire model is differentiable, and linearized models, which decouple feature transformation from message passing.

Experimentation

Experiments conducted on benchmark datasets such as Cora, Citeseer, and Pubmed demonstrate that SR-GNN significantly alleviates the challenges posed by training data bias. For instance, in experiments with biased training sets, SR-GNN was capable of reclaiming at least 40% of the loss in accuracy incurred by biased samples when using a standard GCN. On large-scale datasets like ogb-arxiv, SR-GNN yielded an absolute improvement of 2% over baseline models while mitigating 30% of the training data bias effect.

Additionally, a variety of tests were conducted to evaluate the sensitivity of SR-GNN to hyperparameters, validating the robust performance improvements across different configurations and degrees of distributional bias.

Implications and Future Directions

The introduction of SR-GNN holds substantial practical significance, especially in domains where data labeling is constrained or biased, such as fraud detection and social network analysis. By equipping GNNs with tools to handle non-IID data, SR-GNN broadens the applicability of GNNs to more realistic scenarios.

Theoretically, this paper suggests new avenues for research into domain adaptation within the field of graph-based learning. It sets the stage for further exploration into domain-specific regularization methods and fairness-aware models that align more closely with real-world data distributions.

In summary, SR-GNN represents a notable step towards addressing a critical but often overlooked issue in GNN training. It not only enhances model resilience to biased training data but also opens doors for refined approaches in transfer learning and domain adaptation in graph-based machine learning systems.