Policy-GNN: Aggregation Optimization for Graph Neural Networks

TL;DR

Policy-GNN introduces a reinforcement learning-based framework that adaptively determines the optimal number of aggregation steps for each node in a graph, significantly improving GNN performance on benchmark datasets.

Contribution

It proposes a novel meta-policy framework using deep reinforcement learning to optimize node-specific aggregation in GNNs, addressing the challenge of diverse node requirements.

Findings

Outperforms state-of-the-art GNN methods on benchmark datasets

Effectively adapts aggregation depth for individual nodes

Enhances GNN accuracy and efficiency

Abstract

Graph data are pervasive in many real-world applications. Recently, increasing attention has been paid on graph neural networks (GNNs), which aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors with stackable network modules. Motivated by the observation that different nodes often require different iterations of aggregation to fully capture the structural information, in this paper, we propose to explicitly sample diverse iterations of aggregation for different nodes to boost the performance of GNNs. It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features. Moreover, it is not straightforward to derive an efficient algorithm since we need to feed the sampled nodes into different number of network layers. To address the above challenges, we propose Policy-GNN, a meta-policy framework that models the sampling procedure and message passing of GNNs into a combined learning process. Specifically, Policy-GNN uses a meta-policy to adaptively determine the number of aggregations for each node. The meta-policy is trained with deep reinforcement learning (RL) by exploiting the feedback from the model. We further introduce parameter sharing and a buffer mechanism to boost the training efficiency. Experimental results on three real-world benchmark datasets suggest that Policy-GNN significantly outperforms the state-of-the-art alternatives, showing the promise in aggregation optimization for GNNs.