GraphANGEL: Adaptive aNd Structure-Aware Sampling on Graph NEuraL Networks

Abstract

Graph neural networks (GNNs) have gained increasing attention in their great success at graph representation learning. In GNNs, the neighborhood of the target node is aggregated iteratively to capture and learn its local structure and neighbor information. Observing that different nodes often require a distinct number of iterations to better learn the representation, we propose an adaptive and structure-aware graph sampling scheme GraphANGEL for GNNs. However, it is quite challenging because both the suitable range of exploration and the important substructure in the neighborhood are difficult to determine. Exploiting the unique feature of random walk mixing time and various node structural role importance measures, we first propose a lightweight component to flexibly estimate the proper neighborhood exploration depth for each target node. Then we investigate different importance metrics to identify and sample the most structurally critical subgraphs that carry a larger influence in messaging passing. Moreover, since different importance metrics unveil different aspects of the graph, we combine and ensemble various importance measures with attention to boost the final performance. In this manner, our method adaptively and explicitly embeds the structural importance information of a node and its critical neighborhood at the same time for finer structure-aware graph representation learning. Evaluation on the benchmark datasets suggests the competitive performance of GraphANGEL to the state-of-the-art approaches, demonstrating the effectiveness of our adaptive and structure-aware sampling approach.