Dyadic Event Attribution in Social Networks with Mixtures of Hawkes Processes.
Dyadic Event Attribution in Social Networks with Mixtures of Hawkes Processes.
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DOI：
10.1145/2505515.2505609
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Zha H
中科院分区：
文献类型：
--
作者：
Li L;Zha H
在社交网络分析的许多应用中，对行为者对之间的交互进行建模并推断影响力非常重要，这导致了近年来引起越来越多兴趣的二元事件建模问题。在本文中，我们重点关注二元事件归因问题，这是二元事件建模中一个重要的缺失数据问题，其中需要根据观察到的时间戳来推断二元事件子集的缺失参与者对。现有的工作要么使用固定的模型参数和启发式规则进行事件归因，要么假设演员对之间的二元事件是独立的。为了解决这些缺点，我们提出了一种基于霍克斯过程混合的概率模型，该模型同时处理事件归因和网络参数推断，同时考虑到共享至少一个参与者的二元事件之间的依赖性。我们还研究使用附加模型来合并正则化以避免过度拟合。我们对国际武装冲突的合成数据集和真实世界数据集的实验表明，与最先进的二元事件归因相比，所提出的新方法能够显着提高准确性。
In many applications in social network analysis, it is important to model the interactions and infer the influence between pairs of actors, leading to the problem of dyadic event modeling which has attracted increasing interests recently. In this paper we focus on the problem of dyadic event attribution, an important missing data problem in dyadic event modeling where one needs to infer the missing actor-pairs of a subset of dyadic events based on their observed timestamps. Existing works either use fixed model parameters and heuristic rules for event attribution, or assume the dyadic events across actor-pairs are independent. To address those shortcomings we propose a probabilistic model based on mixtures of Hawkes processes that simultaneously tackles event attribution and network parameter inference, taking into consideration the dependency among dyadic events that share at least one actor. We also investigate using additive models to incorporate regularization to avoid overfitting. Our experiments on both synthetic and real-world data sets on international armed conflicts suggest that the proposed new method is capable of significantly improve accuracy when compared with the state-of-the-art for dyadic event attribution.