Publications

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Author [ Title(Desc)] Year
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A
Kimmig, A., Memory, A., Miller, R. J. & Getoor, L. A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence. TKDE 31, 1426--1439 (2019).PDF icon kimming-tkde19.pdf (713.64 KB)
Augustine, E. & Getoor, L. A Comparison of Bottom-Up Approaches to Grounding for Templated Markov Random Fields. SysML (2018). at <https://github.com/eriq-augustine/grounding-experiments>PDF icon augustine-sysml18.pdf (624.33 KB)
Farnadi, G., Babaki, B. & Getoor, L. A Declarative Approach to Fairness in Relational Domains. TCDE-Bulletin 42, 36--48 (2019).PDF icon farnadi-de19.pdf (365.14 KB)
Farnadi, G., Kouki, P., Thompson, S. K., Srinivasan, S. & Getoor, L. A Fairness-aware Hybrid Recommender System. RecSys Workshop on FATREC (2018).PDF icon farnadi-fatrec18.pdf (474.04 KB)
Islamaj, R., Getoor, L. & Wilbur, J. A Feature Generation Algorithm with Applications to Biological Sequence Classification. Computational Methods of Feature Selection 1, 355--376 (Chapman and Hall/CRC Press, 2008).PDF icon islamaj-book08.pdf (3.09 MB)
Huang, B., Kimmig, A., Getoor, L. & Golbeck, J. A Flexible Framework for Probabilistic Models of Social Trust. SBP (2013).PDF icon huang-sbp13.pdf (247.2 KB)
Sridhar, D., Fakhraei, S. & Getoor, L. A Probabilistic Approach for Collective Similarity-based Drug-Drug Interaction Prediction. Bioinformatics 32, (2016).PDF icon sridhar-bioinformatics16.pdf (1.94 MB)
Ramesh, A. A Probabilistic Approach to Modeling Socio-Behavioral Interactions. (2016).PDF icon ramesh-thesis16.pdf (865.41 KB)
Kimmig, A., Bach, S., Broecheler, M., Huang, B. & Getoor, L. A Short Introduction to Probabilistic Soft Logic. NIPS Workshop on PPFA (2012).PDF icon kimming-ppfa12.pdf (164.6 KB)
Namata, G., Sharara, H. & Getoor, L. A Survey of Link Mining Tasks for Analyzing Noisy and Incomplete Networks. Link Mining: Models, Algorithms, and Applications 1, 107--133 (Springer, 2010).PDF icon namata-book10.pdf (656.83 KB)
Grycner, A., Weikum, G., Pujara, J., Foulds, J. & Getoor, L. A Unified Probabilistic Approach for Semantic Clustering of Relational Phrases. NeurIPS (2014).
Bilgic, M. & Getoor, L. Active Inference for Collective Classification. Twenty-Fourth Conference on Artificial Intelligence (AAAI NECTAR Track) 1652–1655 (2010).PDF icon bilgic-aaai10.pdf (387.53 KB)
Daozheng, C. et al. Active Inference for Retrieval in Camera Networks. IEEE Workshop on Person-Oriented Vision (2011).PDF icon chen-wpov11.pdf (1.53 MB)
Sharara, H., Getoor, L. & Norton, M. An Active Learning Approach for Identifying Key Opinion Leaders. The 2nd Workshop on Information in Networks (WIN) (2010).
Bilgic, M., Mihalkova, L. & Getoor, L. Active Learning for Networked Data. Proceedings of the 27th International Conference on Machine Learning (ICML-10) (2010).PDF icon bilgic-icml10.pdf (515.65 KB)
Sharara, H., Getoor, L. & Norton, M. Active Surveying. NIPS Workshop on Networks Across Disciplines in Theory and Applications (2010).
Hossam, S., Getoor, L. & Myra, N. Active Surveying: A Probabilistic Approach for Identifying Key Opinion Leaders. IJCAI (2011).PDF icon sharara-ijcai11.pdf (349.39 KB)
Sharara, H., Norton, M. & Getoor, L. Active Surveying for Leadership Identification. The International Sunbelt Social Networks Conference XXX (2010).
Fakhraei, S., Dhanya, S., Pujara, J. & Getoor, L. Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks. KDD (ACM SIGKDD, 2016).
Embar, V., Farnadi, G., Pujara, J. & Getoor, L. Aligning Product Categories using Anchor Products. KBCOM (2018).PDF icon embar-kbcom18.pdf (577.65 KB)
C
Kang, H., Getoor, L. & Singh, L. C-GROUP: A Visual Analytic Tool for Pairwise Analysis of Dynamic Group Membership. Visual Analytics Science and Technology (VAST) (2007).PDF icon vast07-kang.pdf (663.26 KB)
Licamele, L., Bilgic, M., Getoor, L. & Roussopoulos, N. Capital and Benefit in Social Networks. ACM SIGKDD Workshop on Link Analysis and Group Detection (LinkKDD) (2005).PDF icon licamele_linkkdd05.pdf (421.14 KB)
Muthiah, S. et al. Capturing Planned Protests from Open Source Indicators. AI Mag 37, 63–75 (2016).PDF icon muthiah-aimag16.pdf (1.23 MB)
Doppa, J., Yu, J., Tadepalli, P. & Getoor, L. Chance-Constrained Programs for Link Prediction. NIPS Workshop on Analyzing Networks and Learning with Graphs (2009).PDF icon doppa-nips09wkshp.pdf (161.38 KB)
Islamaj, R., Getoor, L. & W. Wilbur, J. Characterizing RNA secondary-structure features and their effects on splice-site prediction. IEEE ICDM Workshop on Mining and Management of Biological Data (2007).
Chang, J., Chen, R., Pujara, J. & Getoor, L. Clustering System Data using Aggregate Measures. SysML (2018).PDF icon chang-sysml18.pdf (299.32 KB)
Pujara, J. & Getoor, L. Coarse-to-Fine, Cost-Sensitive Classification of E-Mail. NIPS Workshop on Coarse-to-Fine Processing (2010).PDF icon pujara_nips10.pdf (258.86 KB)
Zheleva, E., Sharara, H. & Getoor, L. Co-evolution of Social and Affiliation Networks. 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2009).PDF icon fp659-zheleva.pdf (900 KB)
Lansky, A., Friedman, M., Getoor, L., Schmidler, S. & Jr., N. Short. The Collage/Khoros Link: Planning for Image Processing Tasks. Proceedings of the AAAI Spring Symposium on Integrated Planning Applications (1995).
London, B. et al. Collective Activity Detection using Hinge-loss Markov Random Fields. CVPR Workshop on SPTLE (2013).PDF icon london-sptle13.pdf (705.87 KB)
Embar, V., Pujara, J. & Getoor, L. Collective Alignment of Large-scale Ontologies. AKBC Workshop on FKBs (2019).
Sen, P., Namata, G. Mark, Bilgic, M. & Getoor, L. Collective Classification. Encyclopedia of Machine Learning (2010).
Namata, G., Sen, P., Bilgic, M. & Getoor, L. Collective Classification for Text Classification. Text Mining: Classification, Clustering, and Applications 1, 51--69 (Taylor and Francis Group, 2009).PDF icon namata-book09.pdf (4.35 MB)
Sen, P. et al. Collective Classification in Network Data. AI Magazine 29, 93–106 (2008).PDF icon sen-aimag08.pdf (497.82 KB)
London, B. & Getoor, L. Collective Classification of Network Data. Data Classification: Algorithms and Applications 1, 399--416 (CRC Press, 2013).PDF icon london-book13.pdf (394.37 KB)
Bhattacharya, I. & Getoor, L. Collective Entity Resolution In Relational Data. ACM Transactions on Knowledge Discovery from Data 1, 1-36 (2007).PDF icon bhattacharya-tkdd.pdf (346.13 KB)
Bhattacharya, I. Collective Entity Resolution In Relational Data. (2006).PDF icon thesis.pdf (761.21 KB)
Kouki, P., Pujara, J., Marcum, C., Koehly, L. & Getoor, L. Collective Entity Resolution in Familial Networks. IEEE International Conference on Data Mining (ICDM) (2017). at <https://github.com/pkouki/icdm2017>PDF icon kouki-icdm17.pdf (653.4 KB)
Kouki, P., Pujara, J., Marcum, C., Koehly, L. & Getoor, L. Collective Entity Resolution in Multi-Relational Familial Networks. KAIS 61, 1547-–1581 (2018).PDF icon kouki-kais18.pdf (1.17 MB)
Bhattacharya, I. & Getoor, L. Collective Entity Resolution in Relational Data. Data Engineering Bulletin 29, (2006).
Namata, G., Kok, S. & Getoor, L. Collective Graph Identification. KDD (2011).PDF icon namata-kdd11.pdf (185.7 KB)
Namata, G., London, B. & Getoor, L. Collective Graph Identification. TKDD 10, (2015).PDF icon namata-tkdd15.pdf (500.96 KB)
Fakhraei, S., Huang, B. & Getoor, L. Collective Inference and Multi-Relational Learning for Drug–Target Interaction Prediction. NIPS Workshop on MLCB (2013).PDF icon fakhraei-mlcb13.pdf (243.86 KB)
Kimmig, A., Memory, A., Miller, R. & Getoor, L. A Collective, Probabilistic Approach to Schema Mapping. International Conference on Data Engineering (ICDE) (2017). at <https://github.com/alexmemory/kimmig-icde17/wiki>PDF icon kimmig-icde17.pdf (463.69 KB)

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