Personalized Layer Selection for Graph Neural Networks

TL;DR

This paper introduces MetSelect, a method that personalizes layer selection for each node in GNNs, improving classification accuracy and robustness by adapting to local neighborhood properties.

Contribution

It proposes a novel algorithm for selecting optimal GNN layers per node, enabling more adaptive and personalized graph representations.

Findings

Significant accuracy improvements on 10 datasets

Enhanced robustness to poisoning attacks

GNNs can be deeper with personalized layer selection

Abstract

Graph Neural Networks (GNNs) combine node attributes over a fixed granularity of the local graph structure around a node to predict its label. However, different nodes may relate to a node-level property with a different granularity of its local neighborhood, and using the same level of smoothing for all nodes can be detrimental to their classification. In this work, we challenge the common fact that a single GNN layer can classify all nodes of a graph by training GNNs with a distinct personalized layer for each node. Inspired by metric learning, we propose a novel algorithm, MetSelect1, to select the optimal representation layer to classify each node. In particular, we identify a prototype representation of each class in a transformed GNN layer and then, classify using the layer where the distance is smallest to a class prototype after normalizing with that layer's variance. Results on 10 datasets and 3 different GNNs show that we significantly improve the node classification accuracy of GNNs in a plug-and-play manner. We also find that using variable layers for prediction enables GNNs to be deeper and more robust to poisoning attacks. We hope this work can inspire future works to learn more adaptive and personalized graph representations.