IPHITS: An Incremental Latent Topic Model for Link Structure

DOI: 10.1007/978-3-642-04769-5_21
Source: DBLP


The structure of linked documents is dynamic and keeps on changing. Even though different methods have been proposed to exploit
the link structure in identifying hubs and authorities in a set of linked documents, no existing approach can effectively
deal with its changing situation. This paper explores changes in linked documents and proposes an incremental link probabilistic
framework, which we call IPHITS. The model deals with online document streams in a faster, scalable way and uses a novel link
updating technique that can cope with dynamic changes. Experimental results on two different sources of online information
demonstrate the time saving strength of our method. Besides, we make analysis of the stable rankings under small perturbations
to the linkage patterns.

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    ABSTRACT: In this paper, we present a scalable implementation of a topic modeling (Adaptive Link-IPLSA) based method for online event analysis, which summarize the gist of massive amount of changing tweets and enable users to explore the temporal trends in topics. This model also can simultaneously maintain the continuity of the latent semantics to better capture the time line development of events. With the help of this model, users can quickly grasp major topics in these twitters. The preliminary results show that our method leads to more balanced and comprehensive improvement for online event detection compared to benchmark approaches. Additionally our algorithm is computationally feasible in near real-time scenarios making it an attractive alternative for capturing the rapidly changing dynamics of microblogs.
    Software Engineering, Artificial Intelligence, Networking and Parallel & Distributed Computing (SNPD), 2012 13th ACIS International Conference on; 01/2012