Detecting Geographical Competitive Structure for POI Visit Dynamics
Detecting Geographical Competitive Structure for POI Visit Dynamics
复制标题
检测 POI 访问动态的地理竞争结构
DOI：
10.1007/978-3-030-65351-4_3
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
and Masahiro Kimura
中科院分区：
文献类型：
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作者：
Teru Fujii;Masahito Kumano;Joao Gama;and Masahiro Kimura
我们提供了一个框架，用于分析地理影响网络对城市中一组兴趣点(POI)的访问事件序列的影响。由于相互激励的Hawkes过程能够自然地对时态事件数据进行建模并捕捉这些事件之间的交互作用，前人提出了一种基于Hawkes过程的概率模型，称为CHP模型，用于从在线项目的共享事件序列中发现它们之间的合作结构。基于Hawkes过程，我们提出了一种新的概率模型，称为RH模型，用于检测POI集合中的地理竞争结构，并给出了一种从POI访问事件历史推断它的方法。我们从数学上推导了预测RH模型中每个POI的流行程度的解析近似公式，并对CHP模型进行了扩展以提取地理合作结构。利用合成数据，我们首先验证了推理方法的有效性和近似公式的有效性。利用基于位置的社会网络(LBSNs)的实际数据，我们论证了RH模型在预测未来事件方面的重要性，并从地理竞争和合作结构的角度揭示了潜在的地理影响网络。
We provide a framework for analyzing geographical influence networks that have impacts on visit event sequences for a set of point-of-interests (POIs) in a city. Since mutually-exciting Hawkes processes can naturally model temporal event data and capture interactions between those events, previous work presented a probabilistic model based on Hawkes processes, called CHP model, for finding cooperative structure among online items from their share event sequences. In this paper, based on Hawkes processes, we propose a novel probabilistic model, calledRH model, for detecting geographical competitive structure in the set of POIs, and present a method of inferring it from the POI visit event history. We mathematically derive an analytical approximation formula for predicting the popularity of each of the POIs for the RH model, and also extend the CHP model so as to extract geographical cooperative structure. Using synthetic data, we first confirm the effectiveness of the inference method and the validity of the approximation formula. Using real data of Location-Based Social Networks (LBSNs), we demonstrate the significance of the RH model in terms of predicting the future events, and uncover the latent geographical influence networks from the perspective of geographical competitive and cooperative structures.