Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective
<|reference_start|>Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective: How would randomly shuffling feature vectors among nodes from the same class affect graph neural networks (GNNs)? The feature shuffle, intuitively, perturbs the dependence between graph topology and features (A-X dependence) for GNNs to learn from. Surprisingly, we observe a consistent and significant improvement in GNN performance following the feature shuffle. Having overlooked the impact of A-X dependence on GNNs, the prior literature does not provide a satisfactory understanding of the phenomenon. Thus, we raise two research questions. First, how should A-X dependence be measured, while controlling for potential confounds? Second, how does A-X dependence affect GNNs? In response, we (i) propose a principled measure for A-X dependence, (ii) design a random graph model that controls A-X dependence, (iii) establish a theory on how A-X dependence relates to graph convolution, and (iv) present empirical analysis on real-world graphs that align with the theory. We conclude that A-X dependence mediates the effect of graph convolution, such that smaller dependence improves GNN-based node classification.<|reference_end|>
@article{lee2024feature,
    title={Feature Distribution on Graph Topology Mediates the Effect of Graph
  Convolution: Homophily Perspective},
    author={Soo Yong Lee, Sunwoo Kim, Fanchen Bu, Jaemin Yoo, Jiliang Tang, Kijung
  Shin},
    journal={arXiv preprint arXiv:2402.04621},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04621},
    primaryClass={cs.LG}
}