Can Modifying Data Address Graph Domain Adaptation? (2407.19311v1)

Abstract: Graph neural networks (GNNs) have demonstrated remarkable success in numerous graph analytical tasks. Yet, their effectiveness is often compromised in real-world scenarios due to distribution shifts, limiting their capacity for knowledge transfer across changing environments or domains. Recently, Unsupervised Graph Domain Adaptation (UGDA) has been introduced to resolve this issue. UGDA aims to facilitate knowledge transfer from a labeled source graph to an unlabeled target graph. Current UGDA efforts primarily focus on model-centric methods, such as employing domain invariant learning strategies and designing model architectures. However, our critical examination reveals the limitations inherent to these model-centric methods, while a data-centric method allowed to modify the source graph provably demonstrates considerable potential. This insight motivates us to explore UGDA from a data-centric perspective. By revisiting the theoretical generalization bound for UGDA, we identify two data-centric principles for UGDA: alignment principle and rescaling principle. Guided by these principles, we propose GraphAlign, a novel UGDA method that generates a small yet transferable graph. By exclusively training a GNN on this new graph with classic Empirical Risk Minimization (ERM), GraphAlign attains exceptional performance on the target graph. Extensive experiments under various transfer scenarios demonstrate the GraphAlign outperforms the best baselines by an average of 2.16%, training on the generated graph as small as 0.25~1% of the original training graph.

• The paper proposes GraphAlign, a novel data-centric approach that modifies source graphs to mitigate domain shifts in unsupervised graph domain adaptation.
• It employs an alignment principle using Maximum Mean Discrepancy and a rescaling strategy that preserves critical information in substantially smaller graphs.
• The method achieves an average improvement of 2.16% across diverse transfer scenarios, demonstrating significant practical benefits over model-centric techniques.

An Expert Overview of "Can Modifying Data Address Graph Domain Adaptation?"

The paper explores the limitations of model-centric approaches in Unsupervised Graph Domain Adaptation (UGDA) and proposes a data-centric methodology. While Graph Neural Networks (GNNs) have demonstrated considerable success in graph-based tasks, their performance often degrades in scenarios involving distributional shifts between the source and target domains. The focus of this research shifts from traditionally model-centric approaches that emphasize domain-invariant learning and novel architecture design towards leveraging the latent potential of data-centric strategies, particularly modifying the source data itself to enhance transferability.

Contributions

This research proposes a novel UGDA method termed GraphAlign, emphasizing two primary data-centric principles derived from revisiting theoretical generalization bounds for UGDA:

1. Alignment Principle: This principle focuses on minimizing the distributional discrepancy between the modified source graph and the target graph. GraphAlign achieves this through Maximum Mean Discrepancy (MMD) rather than the computationally prohibitive Wasserstein distance.
2. Rescaling Principle: It argues for reducing the scale of the source graph without losing critical information, thereby improving computational efficiency. The theoretical justification is anchored in the paper of generalization bounds, with experiments showing competitive performance with graphs as small as 0.25-1% of the original size.

Methodology

GraphAlign generates a smaller, transferable graph by selectively resampling the source graph's nodes. This approach involves:

• Mapping graphs to Euclidean representations using a surrogate GNN model to facilitate efficient computation of distributional characteristics.
• Aligning the representation distributions between the source and target graphs to minimize domain shift.
• Simulating the learning process on the modified graph using classic Empirical Risk Minimization (ERM).

Results and Implications

Extensive experiments across diverse transfer scenarios (including social, biological, and citation networks) demonstrate that GraphAlign achieves an average performance improvement of 2.16% over existing UGDA models. Notably, these results suggest the effectiveness of data-centric UGDA approaches in settings characterized by both significant feature and structural shifts.

The potential implications are profound, impacting both theoretical insights and practical applications. GraphAlign offers a pathway to specialized, domain-specific GNN training without necessitating novel architectures or complex domain-invariant representations. From a theoretical angle, embracing data-centric perspectives could spawn further research in circumventing inherent limitations of model-centric strategies. Practically, adapting source graphs could improve GNN deployment in real-world scenarios involving frequent domain shifts, such as dynamic social networks and evolving biochemical interactions.

Future Directions

The investigation points towards a broader adoption of data-centric strategies in varying AI contexts, suggesting that model-centric paradigms can be complemented or even replaced by intelligent data modifications. Further exploration of efficient data rescaling strategies and understanding their limits on UGDA tasks represent promising research trajectories. Additionally, examining the utility and integration of data-centric methods with existing domain adaptation techniques could illuminate synergies between these methodologies.

In sum, this work leverages the alteration of source data as a formidable tool for tackling the challenges of graph domain adaptation, marking a significant contribution to both the theoretical frameworks and practical strategies for GNN implementations.