Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Mining the Web: Discovering Knowledge from HyperText Data
Mining the Web: Discovering Knowledge from HyperText Data
A model for detecting "global footprint anomalies" in a grid environment
PAISI'10 Proceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics
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A graph-theoretic model for incrementally detecting latent associations among terms in a document corpus is presented. The algorithm is based on an energy model that quantifies similarity in context between pairs of terms. Latent associations that are established in turn contribute to the energy of their respective contexts. The proposed model avoids the polysemy problem where spurious associations across terms in different contexts are established due to the presence of one or more common polysemic terms. The algorithm works in an incremental fashion where energy values are adjusted after each document is added to the corpus. This has the advantage that computation is localized around the set of terms contained in the new document, thus making the algorithm run much faster than conventional matrix computations used for singular value decompositions.