Assessing Document Relevance by Modeling Citation Networks with Probabilistic Graphs

Author:

Basile Teresa M.A.,Di Mauro Nicola,Esposito Floriana

Publisher

Elsevier BV

Subject

General Engineering

Reference18 articles.

1. Getoor, L., Taskar, B. Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning). The MIT Press; 2007. ISBN 0262072882.

2. k-nearest neighbors in uncertain graphs;Potamias;Proc VLDB Endow,2010

3. Zou, Z., Gao, H., Li, J. Discovering frequent subgraphs over uncertain graph databases under probabilistic semantics. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM; 2010, p. 633-642.

4. Pfeiffer III, J.J., Neville, J. Methods to determine node centrality and clustering in graphs with uncertain structure. In: Proceedings of the Fifth International Conference on Weblogs and Social Media. The AAAI Press; 2011,.

5. Taranto, C., Di Mauro, N., Esposito, F. Learning in probabilistic graphs exploiting language-constrained patterns. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z., editors. New Frontiers in Mining Complex Patterns; vol. 7765 of LNCS. Springer Berlin Heidelberg; 2013, p. 155-169.

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