Incomplete Graph Representation and Learning via Partial Graph Neural Networks

Abstract

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.

References in corpus (6)

• Graph Neural Networks: A Review of Methods and Applications
• Variational Graph Auto-Encoders
• FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
• Learning on Attribute-Missing Graphs
• Graph Convolutional Networks for Graphs Containing Missing Features
• Robust Principal Component Analysis on Graphs

Cited by in corpus (2)

• On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features
• Full-Body Motion Reconstruction with Sparse Sensing from Graph Perspective