Social Network Mining with Nonparametric Relational Models
Social Network Mining with Nonparametric Relational Models
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DOI：
10.1007/978-3-642-14929-0_5
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发表时间：
2008-08
期刊：
影响因子：
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通讯作者：
Zhao Xu;Volker Tresp;Achim Rettinger;K. Kersting
中科院分区：
文献类型：
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作者：
Zhao Xu;Volker Tresp;Achim Rettinger;K. Kersting
统计关系学习(SRL)为分析具有丰富对象集合和复杂网络的社会网络数据提供了有效的技术。无限隐关系模型(IHRM)将非参数混合模型引入到关系学习中，并在许多关系应用中取得了成功。本文探讨了利用IHRMS对复杂社会网络进行建模和分析，以进行社区检测、链接预测和产品推荐。在基于IHRM的社交网络模型中，每条边都与一个随机变量相关联，这些随机变量之间的概率依赖关系由模型基于关系结构指定。隐藏变量(每个对象一个)能够传输信息，从而可以获得非局部概率依赖关系。该模型可用于预测实体属性，预测实体之间的关系，并执行可解释的聚类分析。我们通过三个社交网络应用程序展示了IHRM的性能。我们对桑普森修道院的数据进行了社区分析，并对Bernard&Killworth数据进行了链接分析。最后，我们将IHRM应用于MovieLens数据，以预测用户对电影的偏好，并对用户集群和电影集群进行分析。
Statistical relational learning (SRL) provides effective techniques to analyze social network data with rich collections of objects and complex networks. Infinite hidden relational models (IHRMs) introduce nonparametric mixture models into relational learning and have been successful in many relational applications. In this paper we explore the modeling and analysis of complex social networks with IHRMs for community detection, link prediction and product recommendation. In an IHRM-based social network model, each edge is associated with a random variable and the probabilistic dependencies between these random variables are specified by the model, based on the relational structure. The hidden variables, one for each object, are able to transport information such that non-local probabilistic dependencies can be obtained. The model can be used to predict entity attributes, to predict relationships between entities and it performs an interpretable cluster analysis. We demonstrate the performance of IHRMs with three social network applications. We perform community analysis on the Sampson’s monastery data and perform link analysis on the Bernard & Killworth data. Finally we apply IHRMs to the MovieLens data for prediction of user preference on movies and for an analysis of user clusters and movie clusters.