摘要
We address the problem of predicting missing links for a social network in Social Media by using user activity data. We propose a simple and natural probabilistic model with latent features (traits) for simultaneously generating links and activities in the set of nodes, and present an efficient method of learning the model from the observed links and activities. In order to estimate the total number of latent features and the probability distribution of them for each node from the observed data, we incorporate a hierarchical Dirichlet process (HDP) into the model. On the basis of the learned model, we present a method of predicting missing links in the social network. We experimentally show by using synthetic data that the proposed learning method can estimate the link creation probabilities in good accuracy when there is an enough amount of training data. Moreover, using real and synthetic data, we experimentally demonstrate the effectiveness of the proposed link prediction method.
摘要译文
我们应对使用用户活动数据预测缺失的环节在社会化媒体社交网络的问题。我们提出与潜特征（性状）的简单和自然的概率模型，用于同时产生在该组的节点的链接和活动，并呈现学习从所观察到的链路和活动的模型的一个有效方法。为了估计的潜特征的总数，其中，用于从观察到的数据中的每个节点的概率分布，我们引入一个分层狄利克雷过程（HDP）到模型中。在得知模型的基础上，我们提出了预测社交网络中的缺失环节的方法。我们实验通过使用所提出的学习方法可以估算在良好的精度链路创建概率时，有一个足够的训练数据的合成数据显示。此外，使用真实的和合成的数据，我们通过实验证明了该链路的预测方法的有效性。
Kamei, T.;Ono, K.;Kumano, M.;Kimura, M.. Predicting missing links in social networks with hierarchical dirichlet processes[C]//Neural Networks (IJCNN), The 2012 International Joint Conference on, Brisbane, QLD, 10-15 June 2012, IEEE, 2012: 1-8