ASSL-HGAT: Active semi-supervised learning empowered heterogeneous graph attention network

Abstract

Recently, heterogeneous graph attention network (HGAT) has been widely applied to various machine learning tasks and achieved remarkable results with sufficient labeled data. However, it is noteworthy that in many tasks, labeled data is scarce and the data labeling process is expensive. To that end, this paper presents a novel framework for learning from data with limited labels by organically integrating active learning (AL) and semi-supervised learning (SSL) into heterogeneous graph network. Our framework consists of three components: heterogeneous information network (HIN), HGAT and active semi-supervised learning strategy (ASSL). The adjustable HIN is first constructed by fusing multi-modal features. Then the HGAT is used to encode HIN and the double-layer attention mechanism can mitigate noises during information fusion. The ASSL is further designed to enrich the high-quality labeled training data for model train. Samples selected by uncertainty-aware AL and samples labeled by pseudo-label selection based SSL are finally mixed to iteratively train the ASSL-HGAT framework. Experimental results prove that ASSL-HGAT surpasses the compared state-of-the-art methods on five representative semi-supervised benchmark datasets under the same experimental settings.