The Time-Series Link Prediction Problem with Applications in Communication Surveillance

Published Online:https://doi.org/10.1287/ijoc.1080.0292

References

  • Adamic L., Adar E. Friends and neighbors on the Web. Soc. Networks (2003) 25(3):211–230CrossrefGoogle Scholar
  • Akaike H. A new look at the statistical model identification. IEEE Trans. Automatic Control (1974) 19(6):716–723CrossrefGoogle Scholar
  • Alwan L. C., Roberts H. V. Time-series modeling for statistical process control. J. Bus. Econom. Statist. (1988) 6(1):87–95CrossrefGoogle Scholar
  • Barabasi A.-L., Albert R. Emergence of scaling in random networks. Science (1999) 286(5439):509–512CrossrefGoogle Scholar
  • Berger-Wolf T. Y., Saia J. A framework for analysis of dynamic social networks. Proc. 12th ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (2006) Philadelphia(ACM, New York) 523–528CrossrefGoogle Scholar
  • Bolot J.-C., Hoschka P. Performance engineering of the World Wide Web: Application to dimensioning and cache design. Comput. Networks ISDN Systems (1996) 28(7–11):1397–1405CrossrefGoogle Scholar
  • Box G. E. P., Jenkins G.Time Series Analysis, Forecasting, and Control (1970) (Holden-Day, Oakland, CA) Google Scholar
  • Bradley A. P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition (1997) 30(7):1145–1159CrossrefGoogle Scholar
  • Dempster A., Laird N., Rubin D. Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. Ser. B (1977) 39(1):1–38Google Scholar
  • Domingos P., Richardson M. Mining the network value of customers. Proc. Seventh ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (2001) San Francisco(ACM, New York) 57–66CrossrefGoogle Scholar
  • Dzeroski S., Lavrac N.Relational Data Mining (2001) (Springer-Verlag, Berlin) CrossrefGoogle Scholar
  • Getoor L. Link mining: A new data mining challenge. SIGKDD Explorations (2003) 5(1):84–89CrossrefGoogle Scholar
  • Getoor L., Sahami M. Using probabilistic relational models for collaborative filtering. Proc. WebKDD'99 (1999) San DiegoGoogle Scholar
  • Getoor L., Friedman N., Koller D., Taskar B. Learning probabilistic models of link structure. J. Machine Learn. Res. (2002) 3:679–707Google Scholar
  • Gilbert P. D. Combining VAR estimation and state space model reduction for simple good predictions. J. Forecasting (1995) 14:229–250CrossrefGoogle Scholar
  • Goldberg D. S., Roth F. P. Assessing experimentally derived interactions in a small world. Proc. National Acad. Sci. (2003) 100(8):4372–4376CrossrefGoogle Scholar
  • Heider F. Attitudes and cognitive organization. J. Psych. (1946) 21:107–112CrossrefGoogle Scholar
  • Hofmann T., Laskey K., Prade H. Probabilistic latent semantic analysis. Proc. 15th Conf. Uncertainty Artificial Intelligence (1999) Stockholm(Morgan Kaufmann, San Fransisco) 289–296Google Scholar
  • Hofmann T. Latent semantic models for collaborative filtering. ACM Trans. Inform. Systems (2004) 22(1):89–115CrossrefGoogle Scholar
  • Holme P., Park S. M., Kim B. J., Edling C. R. Korean university life in a network perspective: Dynamics of a large affiliation network. Physica A (2007) 373:821–830CrossrefGoogle Scholar
  • Huang Z., Chen H., Zeng D. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inform. Systems (2004a) 22(1):116–142CrossrefGoogle Scholar
  • Huang Z., Zeng D., Chen H. A link analysis approach to recommendation with sparse data. Proc. Americas Conf. Inform. Systems (2004b) New York(Association of Information Systems, Atlanta) 1997–2005Google Scholar
  • Huang Z., Chung W., Ong T.-H., Chen H. A graph-based recommender system for digital library. Proc. Second ACM/IEEE-CS Joint Conf. Digital Libraries (2002) Portland, OR(ACM, New York) 65–73Association of Information Systems, AtlantaCrossrefGoogle Scholar
  • Katz L. A new status index derived from sociometric analysis. Psychometrika (1953) 18(1):39–43CrossrefGoogle Scholar
  • Leskovec J., Kleinberg J., Faloutsos C. Graph evolution: Densification and shrinking diameters. ACM Trans. Knowledge Discovery Data (2007) 1(1):2CrossrefGoogle Scholar
  • Liben-Nowell D., Kleinberg J. The link prediction problem for social networks. Proc. 12th Internat. Conf. Inform. Knowledge Management (CIKM) (2003) New Orleans(ACM, New York) 556–559CrossrefGoogle Scholar
  • O'Madadhain J., Hutchins J., Smyth P. Prediction and ranking algorithms for event-based network data. ACM SIGKDD Explorations (2005) 7(2):23–30CrossrefGoogle Scholar
  • Popescul A., Ungar L. Statistical relational learning for link prediction. Proc. Workshop Learn. Statist. Models Relational Data Internat. Joint Conf. Artificial Intelligence (2003) Acapulco, Mexico(ACM, New York) 81–90Google Scholar
  • Porter-Hudak S. An application of the seasonal fractionally differenced model to the monetary aggregates. J. Amer. Statist. Assoc. (1990) 85(410):338–344CrossrefGoogle Scholar
  • Potgieter A., April K. A., Cooke R. J. E., Osunmakinde I. O. Temporality in link prediction: Understanding social complexity. Emergence Complexity Organ. (2009) . ForthcomingGoogle Scholar
  • Rattigan M., Jensen D. The case for anomalous link detection. Proc. 4th Multi-Relational Data Mining Workshop, 11th ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (2005) Chicago(ACM, New York) 69–74CrossrefGoogle Scholar
  • Resnick P., Varian H. Recommender systems. Comm. ACM (1997) 40(3):56–58CrossrefGoogle Scholar
  • Resnick P., Iacovou N., Suchak M., Bergstorm P., Riedl J. GroupLens: An open architecture for collaborative filtering of netnews. Proc. ACM Conf. Comput.-Supported Cooperative Work (1994) (ACM, New York) 175–186CrossrefGoogle Scholar
  • Salton G.Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer (1989) (Addison Wesley, Reading, MA) Google Scholar
  • Sarkar P., Moore A. W. Dynamic social network analysis using latent space models. ACM SIGKDD Explorations (2005) 7(2):31–40CrossrefGoogle Scholar
  • Sarwar B., Karypis G., Konstan J., Riedl J. Application of dimensionality reduction in recommender systems: A case study. Proc. WebKDD Workshop at the ACM SIGKKD (2000) Boston(ACM, New York) CrossrefGoogle Scholar
  • Shetty J., Adibi J. The Enron data set database schema and brief statistical report. (2005) . http://www.isi.edu/∼adibi/Enron/Enron_Dataset_Report.pdfGoogle Scholar
  • Shumway R., Stofer D. S.Time Series Analysis and Its Applications (2000) (Springer-Verlag, New York) CrossrefGoogle Scholar
  • Ungar L. H., Foster D. P. A formal statistical approach to collaborative filtering. Proc. Conf. Automated Learn. Discovery (CONALD) (1998) PittsburghGoogle Scholar
  • Vazquez A., Oliveira J. G., Barabasi A.-L. The inhomogeneous evolution of subgraphs and cycles in complex networks. Phys. Rev. Lett. E (2005) 71:025103CrossrefGoogle Scholar
  • Winkler W. E. Advanced methods for record linkage. (1994) . Technical report, Statistical Research Division, U.S. Census Bureau, Wasington, D.C.Google Scholar
  • Yu K., Chu W., Yu S., Tresp V., Xu Z. Stochastic relational models for discriminative link prediction. Adv. Neural Inform. Processing Systems (2006) 19:1553–1560Google Scholar
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