The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Learning to Probabilistically Identify Authoritative Documents
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Bursty and Hierarchical Structure in Streams
Data Mining and Knowledge Discovery
Model-Based estimation of word saliency in text
DS'06 Proceedings of the 9th international conference on Discovery Science
Deconvolutive clustering of markov states
ECML'06 Proceedings of the 17th European conference on Machine Learning
Web document clustering using hyperlink structures
Computational Statistics & Data Analysis
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We develop and investigate probabilistic approaches of state clustering in higher-order Markov chains. A direct extension of the Aggregate Markov model to higher orders turns out to be problematic due to the large number of parameters required. However, in many cases, the events in the finite memory are not equally salient in terms of their predictive value. We exploit this to reduce the number of parameters. We use a hidden variable to infer which of the past events is the most predictive and develop two different mixed-order approximations of the higher-order aggregate Markov model. We apply these models to the problem of community identification from event sequences produced through online computer-mediated interactions. Our approach bypasses the limitations of static approaches and offers a flexible modelling tool, able to reveal novel and insightful structural aspects of online interaction dynamics.