Exploiting time-varying relationships in statistical relational models
Exploiting time-varying relationships in statistical relational models
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DOI：
10.1145/1348549.1348551
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发表时间：
2007-08
期刊：
影响因子：
--
通讯作者：
Umang Sharan;Jennifer Neville
中科院分区：
文献类型：
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作者：
Umang Sharan;Jennifer Neville
在越来越多的关系域中，数据记录实体之间交互的时间序列。例如，在引文领域，作者每年都会一起发表科学论文，在电话欺诈检测领域，人们每天都会互相打电话。这些相互作用的时间动态包含可以改进预测模型的信息（例如，经常一起发布的人很可能是在同一主题上发布的），但是迄今为止，几乎没有努力将时变依赖性结合到关系模型中。关系学习领域过去的工作主要集中在关系数据的静态“快照”上。在本文中，我们提出了一个初步的方法来建模动态关系数据图的属性预测模型。更具体地说，我们使用一个两步的过程，首先总结动态图与加权静态图，然后将链接权重的关系贝叶斯分类器。我们在Cora数据集上评估了我们的方法（其中合著者和引用链接随时间而变化），结果表明，我们的方法比忽略数据时间分量的基线快照方法具有显着的性能增益。
In a growing number of relational domains, the data record temporal sequences of interactions among entities. For example, in citation domains authors publish scientific papers together each year and in telephone fraud detection domains people make calls to each other each day. The temporal dynamics of these interactions contain information that can improve predictive models (e.g., people publishing together frequently are likely to be publishing on the same topic) but to date there has been little effort to incorporate timevarying dependencies into relational models. Past work in relational learning has focused primarily on static "snapshots" of relational data. In this paper, we present an initial approach to modeling dynamic relational data graphs in predictive models of attributes. More specifically, we use a two-step process that first summarizes the dynamic graph with a weighted static graph and then incorporates the link weights in a relational Bayes classifier. We evaluate our approach on the Cora dataset (where co-author and citation links vary over time) showing that our approach results in significant performance gains over a baseline snapshot approach that ignores the temporal component of the data.