Semi-supervised Node Importance Estimation with Informative Distribution Modeling for Uncertainty Regularization

Yankai Chen, Taotao Wang, Yixiang Fang, Yunyu Xiao

Track: Social networks and social media

Keywords: Node Importance Estimation, Semi-supervised Learning, Heterogeneous Graph

Abstract: Graph node importance estimation, a classical problem in network analysis, underpins various web applications. To improve estimation accuracy, previous methods either exploit intrinsic topological characteristics, e.g., graph centrality, or leverage additional information, e.g., data heterogeneity, for node feature enhancement. However, these methods follow the supervised learning setting, overlooking the fact that ground-truth node-importance data are usually partially labeled in practice. In this work, we propose the first semi-supervised node importance estimation framework, i.e., EASING, to improve learning quality for unlabeled data in heterogeneous graphs. Different from previous approaches, EASING explicitly captures uncertainty to reflect the confidence of model predictions. To jointly estimate the importance values and uncertainties, EASING incorporates DJE, a deep encoder-decoder neural architecture. DJE introduces distribution modeling for graph nodes, where the distribution representations are decoded to derive both importance and uncertainty estimates, after encoding the rich heterogeneous graph information. Additionally, DJE facilitates effective pseudo-label generation for the unlabeled data to enrich the training samples. Then based on both labeled and pseudo-labeled data, EASING develops effective semi-supervised heteroscedastic learning with the varying node uncertainty regularization. Extensive experiments on three real-world datasets highlight the superior performance of EASING compared to competing methods and demonstrate the effectiveness of each individual module. Codes are available via https://anonymous.4open.science/r/EASING-2F70/.

Submission Number: 1297