Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Leveraging Relational Autocorrelation with Latent Group Models
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Pseudolikelihood EM for Within-network Relational Learning
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining advisor-advisee relationships from research publication networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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A social network consists of people (or other social entities) connected by a set of social relationships. Awareness of the relationship types is very helpful for us to understand the structure and the characteristics of the social network. Traditional classifiers are not accurate enough for relationship labeling since they assume that all the labels are independent and identically distributed. A relational probabilistic model, relational Markov networks (RMNs), is introduced to labeling relationships, but the inefficient parameter estimation makes it difficult to deploy in large-scale social networks. In this paper, we propose a communitybased pseudolikelihood (CBPL) approach for relationship labeling. The community structure of a social network is used to assist in constructing the conditional random field, and this makes our approach reasonable and accurate. In addition, the computational simplicity of pseudolikelihood effectively resolves the time complexity problem which RMNs are suffering. We apply our approach on two real-world social networks, one is a terrorist relation network and the other is a phone call network we collected from encrypted call detail records. In our experiments, for avoiding losing links while splitting a closely connected social network into separate training and test subsets, we split the datasets according to the links rather than the individuals. The experimental results show that our approach performs well in terms of accuracy and efficiency.