Li Liangda, Zha Hongyuan
College of Computing, Georgia Institute of Technology, Atlanta, GA 30032.
Proc ACM Int Conf Inf Knowl Manag. 2013:1667-1672. doi: 10.1145/2505515.2505609.
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.
在社交网络分析的许多应用中，对参与者之间的互动进行建模并推断其相互影响非常重要，这就引出了二元事件建模问题，该问题近来已引起越来越多的关注。在本文中，我们聚焦于二元事件归因问题，这是二元事件建模中一个重要的缺失数据问题，即需要根据观察到的时间戳推断二元事件子集中缺失的参与者对。现有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，要么假设不同参与者对之间的二元事件是独立的。为解决这些不足，我们提出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，同时考虑了至少共享一个参与者的二元事件之间的依赖性。我们还研究使用加法模型来纳入正则化以避免过拟合。我们在国际武装冲突的合成数据集和真实世界数据集上所做的实验表明，与二元事件归因的现有最先进方法相比，所提出的新方法能够显著提高准确性。
