Graph Neural Networks (GNNs) have shown expressive performance on graph representation learning by aggregating information from neighbors. Recently, some studies have discussed the importance of modeling neighborhood distribution on the graph. However, most existing GNNs aggregate neighbors' features through single statistic (e.g., mean, max, sum), which loses the information related to neighbor's feature distribution and therefore degrades the model performance. In this paper, inspired by the method of moment in statistical theory, we propose to model neighbor's feature distribution with multi-order moments. We design a novel GNN model, namely Mix-Moment Graph Neural Network (MM-GNN), which includes a Multi-order Moment Embedding (MME) module and an Element-wise Attention-based Moment Adaptor module. MM-GNN first calculates the multi-order moments of the neighbors for each node as signatures, and then use an Element-wise Attention-based Moment Adaptor to assign larger weights to important moments for each node and update node representations. We conduct extensive experiments on 15 real-world graphs (including social networks, citation networks and web-page networks etc.) to evaluate our model, and the results demonstrate the superiority of MM-GNN over existing state-of-the-art models.
翻译:图像神经网络(GNNs) 通过汇总邻居提供的信息,在图形演示学习中显示了表情表现。最近,一些研究讨论了图中邻居分布模型的重要性。然而,大多数现有的GNNs通过单一统计(例如平均、最大、总)综合邻居特征(例如平均、最大)失去了与邻居特征分布有关的信息,因此降低了模型性能。在本文中,根据统计理论的时针方法,我们提议以多顺序时间模拟邻居特征分布模式。我们设计了一个新型GNN模式,即Mix-Moment神经网络(MMM-GNN),其中包括多顺序嵌入模型模块和基于关注的移动适应模块。MM-GNN首先计算每个节点的邻居多顺序时间作为签名,然后使用基于注意的调控器调器给每个节点的重要时刻分配更大的重量,并更新节点代表。我们在15个真实世界的模型(包括社会网络、现有MMNM的优越性网络)上进行了广泛的实验,并评估了我们现有的模型-MNB的网络。