基于标记点处理的图卷积神经网络

Yongzhe Chang, Xinhang Xiao, Yang Yu
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引用次数: 0

摘要

点过程是给定空间中位置变量的集合,而标记点过程(MPP)基本上是在每个点测量一些附加特征或值的点过程。现有的金融市场模型和住院模型等应用表明,MPPS适用于具有有意义值的随机事件建模。一个很大的挑战是研究不同类型事件之间的关系,或者不同类型的标记如何相互影响。虽然Hawkes方法是一种有效且高效的方法来模拟具有内部影响的点过程,但强度函数的推理方法可能是昂贵的,特别是对于多维过程。图卷积网络(GCN)是一种功能强大的神经网络,用于直接处理图。提出了一种基于遗传神经网络的模型来研究标记过程中的潜在模式,并在不估计Hawkes强度函数的情况下预测未来事件的位置。实验使用了近50年的水管泄漏和破裂记录的真实数据集,模拟为一个标记点过程,其中泄漏和破裂是两种标记形式。在综合数据集和实际水管数据集上的实验结果表明,所提出的模型优于目前最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graph Convolutional Neural Networks based Marked Point Process
A point process is a collection of positional variables in a given space, while a marked point process (MPP) is basically a point process that measures some additional feature or value at each point. Existing applications such as financial market models and hospitalization models have demonstrated that MPPS are suitable for modeling random events with meaningful values. A big challenge is to study the relationship between different types of events, or how different types of markers affect each other. While Hawkes procedures are an effective and efficient way to model point processes with internal influences, inferential methods of intensity functions can be expensive, especially for multi-dimensional processes. Graph convolutional Network (GCN) is a powerful neural network designed to directly process graphs. A GCN-based model is presented to study potential patterns in the marking process and to predict the location of future events without estimating the Hawkes intensity function. A real data set of water pipe leakage and rupture records over the last 50 years was used in the experiment, modeled as a marker point process, where leakage and rupture are two marker forms. Experimental results on synthetic data sets and real water pipe data sets show that the proposed model is superior to recent state-of-the-art methods.
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