Causal Modeling

  • A/B Testing in Networks with Adversarial Members — K. Clary and D. Jensen (2017). In 13th International Workshop on Mining and Learning with Graphs at the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. [PDF]
  • Inferring network effects in relational data — D. Arbour, D. Garant, and D. Jensen (2016). In Proceedings of 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. [PDF]
  • Inferring causal direction from relational data — D. Arbour, K. Marazopoulou, and D. Jensen (2016). In Proceedings of Thirty-second Conference on Uncertainty in Artificial Intelligence. [PDF]
  • Evaluating causal models by comparing interventional distributions — D. Garant and D. Jensen (2016). SIGKDD Workshop on Causation. [PDF]
  • Learning the structure of causal models with relational and temporal dependence — K. Marazopoulou, M. Maier, and D. Jensen (2015). In Proceedings of Thirty-first Conference on Uncertainty in Artificial Intelligence. [PDF]
  • Propensity score matching for causal inference with relational data — D. Arbour, K. Marazopoulou, D. Garant, and D. Jensen (2014). Causal Inference: Learning and Prediction Workshop at UAI. [PDF]
  • Reasoning about independence in probabilistic models of relational data — M. Maier, K. Marazopoulou, and D. Jensen (2014). arXiv:1302.4381. [PDF]
  • A sound and complete algorithm for learning causal models from relational data — M. Maier, K. Marazopoulou, D. Arbour, and D. Jensen (2013). In Proceedings of Twenty-ninth Conference on Uncertainty in Artificial Intelligence. [PDF]
  • Learning causal models of relational domains — M. Maier, B. Taylor, H. Oktay, and D. Jensen (2010). In Proceedings of the Twenty-Forth AAAI Conference on Artificial Intelligence. [PDF]
  • Causal discovery in social media using quasi-experimental designs — H. Oktay, B. Taylor, and D. Jensen (2010). In Proceedings of the SIGKDD Workshop on Social Media Analytics. [PDF]
  • Relational blocking for causal discovery — M. Rattigan, M. Maier, and D. Jensen (2011). In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence. [PDF]
  • Automatic identification of quasi-experimental designs for discovering causal knowledge — D. Jensen, A. Fast, B. Taylor, and M. Maier (2008). In Proceedings of 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. [PDF]

Statistical Relational Learning

  • Refining the semantics of social influence — K. Marazopoulou, D. Arbour, and D. Jensen (2014). In Networks: From Graphs to Rich Data. NIPS Workshop. [PDF]
  • Relational dependency networks — J. Neville and D. Jensen (2007). Journal of Machine Learning Research. 8(Mar): 653-692. [PDF]
  • Why collective inference improves relational classification — D. Jensen, J. Neville, and B. Gallagher (2004). In Proceedings of 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. [PDF]
  • Why stacked models perform effective collective classification — A. Fast and D. Jensen (2008). In Proceedings of the Eighth IEEE International Conference on Data Mining. [PDF]
  • Learning relational probability trees — J. Neville, D. Jensen, L. Friedland, and M. Hay (2003). In Proceedings of 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. [PDF]
  • Simple estimators for relational Bayesian classifiers — J. Neville, D. Jensen, and B. Gallagher (2003). In Proceedings of The 3rd IEEE International Conference on Data Mining. [PDF]
  • Linkage and autocorrelation cause feature selection bias in relational learning — D. Jensen and J. Neville (2002). In Proceedings of the 19th International Conference on Machine Learning. [PDF]
  • Leveraging relational autocorrelation with latent group models — J. Neville and D. Jensen (2005). In Proceedings of the 5th IEEE International Conference on Data Mining. [PDF]

Navigation and Routing in Networks

  • Navigating networks by using homophily and degree — Ö. Şimşek and D. Jensen (2008). PNAS. [PDF]
  • Using structure indices for efficient approximation of network properties — M. Rattigan, M. Maier, and D. Jensen (2006). In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. [PDF]
  • Indexing network structure with shortest-path trees — M. Maier, M. Rattigan, and D. Jensen (2011). ACM Transactions on Knowledge Discovery from Data, 5:3, pp. 1-15. [PDF]
  • MaxProp: Routing for vehicle-based disruption-tolerant networks — J. Burgess, B. Gallagher, D. Jensen, and B. Levine (2006). INFOCOM. [PDF]
  • Creating social networks to improve peer-to-peer networking — A. Fast, D. Jensen, and B. Levine (2005). In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. [PDF]

Privacy and Networks

  • Resisting structural re-identification in anonymized social networks — M. Hay, G. Miklau, D. Jensen, D. Towsley, and L. Chao. (2010) The VLDB Journal, 19:6, pp. 797-823. [PDF]
  • Accurate estimation of the degree distribution of private networks — M. Hay, L. Chao, G. Miklau, and D. Jensen (2009). Proceedings of the 2009 IEEE International Conference on Data Mining. [PDF]
  • Privacy vulnerabilities in encrypted HTTP streams — G. Bissias, M. Liberatore, D. Jensen, and B. Levine (2006). PET. [PDF]

Fraud Detection and Security

  • Using relational knowledge discovery to prevent securities fraud — J. Neville, Ö. Şimşek, D. Jensen, J. Komoroske, K. Palmer, and H. Goldberg (2005). In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. [PDF]
  • Detecting insider threats in a real corporate database of computer usage activity — T. Senator, H. Goldberg, A. Memory, [27 other authors]...D. Corkill, L. Friedland, A. Gentzel, and D. Jensen (2013). In Proceedings of the Nineteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. [PDF]
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