Archived Publications (Latest: https://linqs.github.io/linqs-website/publications/)

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H. Sharara, Sopan, A., Namata, G. Mark, Getoor, L., and Singh, L., G-PARE: A Visual Analytic Tool for Comparative Analysis of Uncertain Graphs, in IEEE Conference on Visual Analytics Science and Technology (VAST), 2011.PDF icon sharara-vast11.pdf (1.64 MB)
H. Sharara, Halgin, D., Getoor, L., and Borgatti, S., Multi-dimensional Trajectory Analysis for Career Histories, in International Sunbelt Social Networks Conference (Sunbelt XXXI), 2011.
H. Sharara, Singh, L., Getoor, L., and Mann, J., Understanding Actor Loyalty to Event-Based Groups in Affiliation Networks, Journal of Advances in Social Networks Analysis and Mining, vol. 1, pp. 115–126, 2011.
H. Sharara, Getoor, L., and Norton, M., Active Surveying, in NIPS Workshop on Networks Across Disciplines in Theory and Applications, 2010.
H. Sharara, Norton, M., and Getoor, L., Active Surveying for Leadership Identification, in The International Sunbelt Social Networks Conference XXX, 2010.
H. Sharara, Getoor, L., and Norton, M., An Active Learning Approach for Identifying Key Opinion Leaders, in The 2nd Workshop on Information in Networks (WIN), 2010.
H. Sharara and Getoor, L., Group Detection, Encyclopedia of Machine Learning, 2010.
H. Sharara, Singh, L., Getoor, L., and Mann, J., The Dynamics of Actor Loyalty to Groups in Affiliation Networks, in International Conference on Advances in Social Networks Analysis and Mining, 2009.PDF icon sharara_asonam09.pdf (446.61 KB)
P. Sen, Namata, G. Mark, Bilgic, M., and Getoor, L., Collective Classification, Encyclopedia of Machine Learning, 2010.
P. Sen, Deshpande, A., and Getoor, L., Read-Once Functions and Query Evaluation in Probabilistic Databases, in International Conference on Very Large Data Bases, 2010.PDF icon draft.pdf (322 KB)
P. Sen, Deshpande, A., and Getoor, L., Bisimulation-based Approximate Lifted Inference, in Uncertainty in Artificial Intelligence, 2009.PDF icon uai09.pdf (240.89 KB)
P. Sen, Deshpande, A., and Getoor, L., PrDB: Managing and Exploiting Rich Correlations in Probabilistic Databases, VLDB Journal, special issue on uncertain and probabilistic databases, 2009.PDF icon sen-vldbj09.pdf (1.12 MB)
P. Sen, Representing and Querying Uncertain Data, University of Maryland, College Park, 2009.PDF icon thesis.pdf (1.12 MB)
P. Sen, Namata, G. Mark, Bilgic, M., Getoor, L., Gallagher, B., and Eliassi-Rad, T., Collective Classification in Network Data, AI Magazine, vol. 29, pp. 93–106, 2008.PDF icon sen-aimag08.pdf (497.82 KB)
P. Sen and Getoor, L., Cost-Sensitive Learning with Conditional Markov Networks, Data Mining and Knowledge Discovery, Special Issue on Utility Based Data Mining, vol. 17, pp. 136–163, 2008.PDF icon draft.pdf (424.09 KB)
P. Sen, Deshpande, A., and Getoor, L., Exploiting Shared Correlations in Probabilistic Databases, in International Conference on Very Large Data Bases, 2008.PDF icon sen-vldb08.pdf (232.29 KB)
P. Sen and Getoor, L., Link-based Classification. University of Maryland, 2007.PDF icon senum-tr07.pdf (511.11 KB)
P. Sen and Deshpande, A., Representing and Querying Correlated Tuples in Probabilistic Databases, in International Conference on Data Engineering, 2007.PDF icon icde07_final.pdf (309.63 KB)
P. Sen, Deshpande, A., and Getoor, L., Representing Tuple and Attribute Uncertainty in Probabilistic Databases, in Workshop on Data Mining of Uncertain Data (ICDM), 2007.PDF icon dune07.pdf (176.67 KB)
P. Sen and Getoor, L., Cost-Sensitive Learning with Conditional Markov Networks, in SIAM Data Mining Workshop on Link Analysis, Counterterrorism and Security, 2006.PDF icon sensiam_lacs06.pdf (137.37 KB)
P. Sen and Getoor, L., Cost-Sensitive Learning with Conditional Markov Networks, in International Conference on Machine Learning, 2006.PDF icon senicml06.pdf (118.33 KB)
P. Sen and Getoor, L., Empirical Comparison of Approximate Inference Algorithms for Networked Data, in ICML Workshop on Statistical Relational Learning (SRL), 2006.PDF icon sensrl06.pdf (225.32 KB)
V. Sehgal, Getoor, L., and Viechnicki, P., Entity Resolution in Geospatial Data Integration, in ACM GIS, 2006.
K. Schnaitter, Polyzotis, N., and Getoor, L., Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications, in International Conference on Very Large Data Bases, 2009.PDF icon schnaitter-vldb09.pdf (743.29 KB)
H. Sayyadi and Getoor, L., Future Rank: Ranking Scientific Articles by Predicting their Future PageRank, in 2009 SIAM International Conference on Data Mining (SDM09), 2009.PDF icon sayyadi_futureRank_sdm09.pdf (621.55 KB)
B. Salami, Parikh, H., Kayali, M., Roy, S., Getoor, L., and Suciu, D., Causal Relational Learning, in International Conference on Management of Data (SIGMOD), 2020.PDF icon salami-sigmod20.pdf (1.02 MB)
B. Saha and Getoor, L., On Maximum Coverage in the Streaming Model & Application to Multi-topic Blog-Watch, in 2009 SIAM International Conference on Data Mining (SDM09), 2009.PDF icon saha-sdm08.pdf (233.12 KB)
B. Saha and Getoor, L., Group Proximity Measure for Recommending Groups in Online Social Networks, in 2nd ACM SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD), 2008.PDF icon kddw-saha.pdf (311.36 KB)
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T. Rekatsinas, Deshpande, A., and Getoor, L., Local Structure and Determinism in Probabilistic Databases, in SIGMOD, 2012.PDF icon rekatsinas-sigmod12.pdf (490.28 KB)
T. Rekatsinas, Dong, X. Luna, Getoor, L., and Srivastava, D., Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration, in 7th Biennial Conference on Innovative Data Systems Research (CIDR `15), 2015.PDF icon rekatsinasCIDR15.pdf (396.99 KB)
T. Rekatsinas, Quality-Aware Data Source Management, University of Maryland, College Park, 2015.
T. Rekatsinas, Ghosh, S., Mekaru, S., Nsoesie, E., Brownstein, J., Getoor, L., and Ramakrishnan, N., SourceSeer: Forecasting Rare Disease Outbreaks Using Multiple Data Sources, in 2015 SIAM International Conference on Data Mining (SDM15), 2015.PDF icon rekatsinasSDM2015.pdf (303.08 KB)
T. Rekatsinas, Deshpande, A., Dong, L., Getoor, L., and Srivastava, D., SourceSight: Enabling Effective Source Selection, in SIGMOD, 2016.PDF icon rekatsinas-sigmod16.pdf (799.94 KB)
T. Rekatsinas, Ghosh, S., Mekaru, S., Nsoesie, E., Brownstein, J., Getoor, L., and Ramakrishnan, N., Forecasting Rare Disease Outbreaks Using Multiple Data Sources, STAT ANAL DATA MIN, 2015.
M. Rastegari, Choi, J., Fakhraei, S., III, H. Daume, and Davis, L., Predictable Dual-View Hashing, in Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013, pp. 1328–1336.PDF icon rastegari13.pdf (2.35 MB)
A. Ramesh, Goldwasser, D., Huang, B., III, H. Daume, and Getoor, L., Learning Latent Engagement Patterns of Students in Online Courses, in Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014.PDF icon ramesh-aaai14.pdf (505.47 KB)
A. Ramesh, Goldwasser, D., Huang, B., III, H. Daume, and Getoor, L., Uncovering Hidden Engagement Patterns for Predicting Learner Performance in MOOCs, in ACM Conference on Learning at Scale, 2014.
A. Ramesh, Goldwasser, D., Huang, B., Daume, III, H., and Getoor, L., Understanding MOOC Discussion Forums using Seeded LDA, in ACL Workshop on Innovative Use of NLP for Building Educational Applications, 2014.PDF icon ramesh-aclws14.pdf (137.57 KB)
A. Ramesh, Goldwasser, D., Huang, B., III, H. Daume, and Getoor, L., Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic, in NIPS Workshop on Data Driven Education, 2013.PDF icon ramesh-nipsws13.pdf (153.92 KB)
A. Ramesh, Rodriguez, M., and Getoor, L., Multi-relational influence models for online professional networks, in International Conference on Web Intelligence (ICWI), 2017, pp. 291-298.PDF icon ramesh-icwi17.pdf (761.17 KB)
A. Ramesh, Rodriguez, M., and Getoor, L., Understanding Influence in Online Professional Networks, in NIPS Workshop on Networks in Social and Information Sciences, 2015.PDF icon ramesh-nipsws15.pdf (211.44 KB)
A. Ramesh, Kumar, S., Foulds, J., and Getoor, L., Weakly Supervised Models of Aspect-Sentiment for Online Course Discussion Forums, in 53rd Annual Meeting of the Association for Computational Linguistics (ACL), 2015.PDF icon ramesh-acl15.pdf (168.7 KB)
A. Ramesh, A Probabilistic Approach to Modeling Socio-Behavioral Interactions, University of Maryland, College Park, 2016.PDF icon ramesh-thesis16.pdf (865.41 KB)
A. Ramesh and Getoor, L., Understanding Evolution of Long-running MOOCs, in International Conference on Web Information Systems Engineering (WISE), 2018.
A. Ramesh, Goldwasser, D., Huang, B., Daume, III, H., and Getoor, L., Interpretable Engagement Models for MOOCs using Hinge-loss Markov Random Fields, IEEE Transactions on Learning Technologies (TLT), vol. 14, pp. 1-1, 2019.PDF icon ramesh-tlt19.pdf (4.3 MB)
N. Ramakrishnan, Butler, P., Self, N., Khandpur, R., Saraf, P., Wang, W., Cadena, J., Vullikanti, A., Korkmaz, G., Kuhlman, C., Marathe, A., Zhao, L., Ting, H., Huang, B., Srinivasan, A., Trinh, K., Getoor, L., Katz, G., Doyle, A., Ackermann, C., Zavorin, I., Ford, J., Summers, K., Fayed, Y., Arredondo, J., Gupta, D., and Mares, D., ‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators, in ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2014.PDF icon ramakrishnan-kdd14.pdf (1.15 MB)

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