Abstract
Current graph topology augmentation methods are mostly static and heavily rely on the assumption of homophily, where connected nodes are presumed to share the same labels by default. Due to the complexity of real-world graphs, the underlying assumption is often disrupted, thus performance declines, demonstrating their limited adaptability. Although learnable methods flexibly change augmentation strategies based on data, ignorance of balancing consistency and diversity leads to suboptimal performance. This gap highlights the need for universally applicable graph augmentation strategies that ensure these two aspects, thereby enhancing model robustness. To address these challenges, we propose the ADAPTGRAPH framework as an adaptive data augmentation method for graphs with different homophily levels. This method can dynamically adjust the graph structure based on a learned conditional distribution with the aid of a graph explainer, thus balancing the consistency and diversity of the augmented data. By framing graph topology modification and model refinement as a joint optimization problem, ADAPTGRAPH facilitates concurrent learning from augmented data and model optimization. Empirical evaluations across diverse benchmarks on node classification tasks reveal that ADAPTGRAPH can be effectively combined with other methods in a plug-and-play manner and consistently yields performance improvement across a diverse set of benchmarks for both homophilic and heterophilic graphs.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
"ADAPTGRAPH: Adaptive Graph Augmentation via Consistency-Diversity Balancing for Homophilic and Heterophilic Graphs", Technical Disclosure Commons, (April 30, 2025)
https://www.tdcommons.org/dpubs_series/8050