Adaptive Sampling-Based Heterogeneous Graph Enhancement

Abstract

In the current research on heterogeneous academic network community detection, there is a widespread challenge of high demand for node representation of node attributes in learning graphs. Particularly, existing methods often perform poorly when dealing with nodes missing attributes. Furthermore, most methods rely on meta-paths, but the optimal length of meta-paths is difficult to determine and the quality of predefined meta-paths directly affects the results. To address this issue, this paper proposes an Adaptive Sampling-based Heterogeneous Graph Enhancement Model (ASGNN). The model aims to solve the problem of inaccurate node representations leading to imprecise community partitions in academic networks. ASGNN first effectively captures the network's topological structure through random walk techniques, and then utilizes an adaptive sampling algorithm to select the most influential adjacent node set, rather than relying on traditional meta-path techniques. The model further employs an attention mechanism to aggregate information from nodes of different types, thereby enhancing attribute completion and topological structure in heterogeneous academic networks. This approach not only fills in missing information but also significantly enhances the semantic and structural integrity of the network. Experimental results demonstrate that the proposed model exhibits outstanding performance on two real datasets compared to baseline models.