Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction

Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
复制标题

多元时空霍克斯过程与网络重建

DOI:
10.1137/18m1226993
复制
发表时间:
2019
影响因子:
3.6
通讯作者:
Porter, Mason A.
Porter, Mason A.
中科院分区:
数学2区
文献类型:
--
作者:
Yuan, Baichuan;Li, Hao;Bertozzi, Andrea L.;Brantingham, P. Jeffrey;Porter, Mason A.

文献摘要

参考文献

被引文献

相似文献

时空数据中往往存在着潜在的网络结构,网络分析工具可以对这些数据产生令人着迷的见解。在本文中,我们开发了一个非参数的方法,从时空数据集使用多变量霍克斯过程的网络重建。与以前的工作网络重建点过程模型,这往往集中在专门的时间信息,我们的方法使用时间和空间信息,并不假设一个特定的参数形式的网络动态。这导致了恢复底层网络的有效方法。我们使用合成网络和我们从现实世界的数据集构建的网络(基于位置的社交媒体网络,犯罪事件的叙述和暴力团伙犯罪)来说明我们的方法。我们的研究结果表明,相比仅使用时间数据,我们的时空方法产生改进的网络重建,提供了有意义的后续分析的基础-如社区结构和图案-重建的网络。
There is often latent network structure in spatial and temporal data, and the tools of network analysis can yield fascinating insights into such data. In this paper, we develop a nonparametric method for network reconstruction from spatiotemporal data sets using multivariate Hawkes processes. In contrast to prior work on network reconstruction with point-process models, which has often focused on exclusively temporal information, our approach uses both temporal and spatial information and does not assume a specific parametric form of network dynamics. This leads to an effective way of recovering an underlying network. We illustrate our approach using both synthetic networks and networks that we construct from real-world data sets (a location-based social-media network, a narrative of crime events, and violent gang crimes). Our results demonstrate that, in comparison to using only temporal data, our spatiotemporal approach yields improved network reconstruction, providing a basis for meaningful subsequent analysis---such as examinations of community structure and motifs---of the reconstructed networks.
DOI: 10.1016/j.ijforecast.2014.01.004
发表时间: 2014-07-01
影响因子: 7.9
作者:
Mohler, George
通讯作者: Mohler, George
DOI: 10.1371/journal.pone.0143638
发表时间: 2015
期刊: PloS one
影响因子: 3.7
作者:
Davies T;Marchione E
通讯作者: Marchione E
DOI: 10.1023/a:1003403601725
发表时间: 1998-06-01
影响因子: 1
作者:
Ogata, Y
通讯作者: Ogata, Y
DOI: 10.1109/tit.2018.2875766
发表时间: 2018-02
影响因子: 2.5
作者:
Benjamin Mark;Garvesh Raskutti;R. Willett
通讯作者: Benjamin Mark;Garvesh Raskutti;R. Willett
犯罪主题建模
DOI: 10.1186/s40163-017-0074-0
发表时间: 2017
期刊: Crime Science
影响因子: 6.1
作者:
Kuang, Da;Brantingham, P. Jeffrey;Bertozzi, Andrea L.
通讯作者: Bertozzi, Andrea L.