Node-Centric Meta Structure Search in Heterogeneous Graphs

Abstract

Heterogeneous graphs are increasingly used to represent complex real-world scenarios with diverse entities and interactions by meta structures. Recently, the search of meta structures is combined with graph neural architecture search to automatically extract the semantic knowledge for various tasks in heterogeneous graphs. However, prior research primarily focuses on identifying meta structures that are universally applicable across all nodes in a graph, neglecting the variations in meta structure selection that arise from the unique features and topology of individual nodes. To address this challenge, we introduce a Node-Centric approach to search Meta Structures in heterogeneous graphs (NC-MS for short). NC-MS implements a node level method that discover meaningful meta structures tailored to each node, capturing subtle differences in meta structure choices between nodes and providing nuanced identification. Additionally, NC-MS utilizes an efficient and differentiable network to enhance operational efficiency. Empirical studies across three real-world datasets validate the superiority of NC-MS, demonstrating its ability to outperform existing models in heterogeneous graph neural networks.