论文标题
带有随机消息传递的置换等值和接近感知的图形神经网络
Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing
论文作者
论文摘要
图形神经网络（GNN）是图形上新兴的机器学习模型。对于GNN而言，置换量等级和接近性意识是非常需要的两个重要特性。需要这两种属性来解决一些具有挑战性的图形问题，例如寻找社区和领导者。在本文中，我们首先在分析上表明，现有的GNN主要基于通讯机制，不能同时保留这两个属性。然后，我们提出了随机消息传递（SMP）模型，这是一种一般且简单的GNN，可维持近端意识和排列量相等。为了保留节点接近，我们可以增强具有随机节点表示的现有GNN。从理论上讲，我们证明该机制可以使GNN能够保留节点接近度，同时，与某些参数化保持置换量相等。我们在十个数据集上报告了广泛的实验结果，并证明了SMP对各种典型图形挖掘任务的有效性和效率，包括图形重建，节点分类和链接预测。
Graph neural networks (GNNs) are emerging machine learning models on graphs. Permutation-equivariance and proximity-awareness are two important properties highly desirable for GNNs. Both properties are needed to tackle some challenging graph problems, such as finding communities and leaders. In this paper, we first analytically show that the existing GNNs, mostly based on the message-passing mechanism, cannot simultaneously preserve the two properties. Then, we propose Stochastic Message Passing (SMP) model, a general and simple GNN to maintain both proximity-awareness and permutation-equivariance. In order to preserve node proximities, we augment the existing GNNs with stochastic node representations. We theoretically prove that the mechanism can enable GNNs to preserve node proximities, and at the same time, maintain permutation-equivariance with certain parametrization. We report extensive experimental results on ten datasets and demonstrate the effectiveness and efficiency of SMP for various typical graph mining tasks, including graph reconstruction, node classification, and link prediction.
