论文标题
通过部分图神经网络不完整的图表表示和学习
Incomplete Graph Representation and Learning via Partial Graph Neural Networks
论文作者
论文摘要
近年来，图形神经网络（GNN）正在越来越关注图数据学习任务。但是，在许多应用程序中，图可能以不完整的形式出现，其中图节点的属性部分未知/缺失。现有的GNN通常是在完整的图表上设计的，这些图形无法直接处理属性 - 完整的图形数据。为了解决这个问题，我们开发了一种基于部分局部聚集的新型GNN，称为部分图神经网络（PAGNNS），用于属性 - 完整的图表表示和学习。我们的工作是出于观察到标准GNN中的邻居聚集功能的动机，可以将其视为邻里重建公式。基于它，我们在不完整的图上定义了两个新型的部分聚集（重建）函数，并为不完整的图形数据学习提供了PAGNN。在几个数据集上进行了广泛的实验证明了拟议的Pagnns的有效性和效率。
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.
