Incomplete Graph Representation and Learning via Partial Graph Neural Networks

• URL: http://arxiv.org/abs/2003.10130v2
• Date: Fri, 4 Jun 2021 09:23:46 GMT
• Title: Incomplete Graph Representation and Learning via Partial Graph Neural Networks
• Authors: Bo Jiang and Ziyan Zhang
• Abstract summary: 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. We develop a novel partial aggregation based GNNs, named Partial Graph Neural Networks (PaGNNs) for attribute-incomplete graph representation and learning.
• Score: 7.227805463462352
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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