2020/03/23 by Bo Jiang, Jiang, Bo, Ziyan Zhang +1 · 6 citations
Computer Science · #Advanced Graph Neural Networks #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Rough Sets and Fuzzy Logic
paper · pdf · doi:10.48550/arxiv.2003.10130
openalex publication_date 2020/03/23 · openalex created_date 2021/06/22 · openalex updated_date 2026/07/28
Graph Neural Networks (GNNs) are gaining increasing attention on graph data learning tasks in recent years. However, in many applications, graph may be coming in an incomplete form where attributes of graph nodes are partially unknown/missing. Existing GNNs are generally designed on complete graphs which can not deal with attribute-incomplete graph data directly. To address this problem, we develop a novel partial aggregation based GNNs, named Partial Graph Neural Networks (PaGNNs), for attribute-incomplete graph representation and learning. Our work is motivated by the observation that the neighborhood aggregation function in standard GNNs can be equivalently viewed as the neighborhood reconstruction formulation. Based on it, we define two novel partial aggregation (reconstruction) functions on incomplete graph and derive PaGNNs for incomplete graph data learning. Extensive experiments on several datasets demonstrate the effectiveness and efficiency of the proposed PaGNNs.