DMNP: A Deep Learning Approach for Missing Node Prediction in Partially Observed Graphs

Abstract

Missing data is unavoidable in graphs, which can significantly affect the accuracy of downstream tasks. Many methods have been proposed to mitigate missing data in partially observed graphs. Most of these approaches assume they have complete access to graph nodes and only focus on recovering missing links, while in practice a part of the graph nodes can also be out of access. This work presents Deep Missing Node Predictor (DMNP), a novel deep learning-based approach to recovering missing nodes in partly observed graphs. Our proposed approach does not rely on additional information that in many cases does not exist. We compare our model with graph completion and deep graph generation baselines. The experimental results show that the DMNP model outperforms previous state-of-the-art approaches.