1. Graph Diffusion Models for Anomaly Detection,
    by Z. Liu, H. Yu, Y. Yan, Z. Hu, P. Rajak, A. Weerasinghe, O. Boz, D. Chakrabarti, and F. Wang, in Machine Learning on Graphs, 2024.
  2. SURE: Robust, Explainable, and Fair Classification without Sensitive Attributes,
    by D. Chakrabarti, in KDD, 2023.
  3. Avoiding Biases due to Similarity Assumptions in Node Embeddings,
    by D. Chakrabarti, in KDD, 2022.
  4. Robust High-Dimensional Classification From Few Positive Examples,
    by D. Chakrabarti, and B. Fauber, in IJCAI, 2022.
  5. Consistent Nonparametric Methods for Network Assisted Covariate Estimation,
    by X. Mao, D. Chakrabarti, and P. Sarkar, in ICML, 2021.
  6. Overlapping Clustering, and One (class) SVM to Bind Them All,
    by X. Mao, P. Sarkar, and D. Chakrabarti, in NIPS, 2018 (spotlight presentation).
  7. Label Propagation with Neural Networks,
    by A. Pal, and D. Chakrabarti, in CIKM, 2018.
  8. Does Market Respond to Information in News Articles beyond Sentiments?,
    by C. Yang, D. Chakrabarti, A. Agarwal, and P. Konana, in CIST, 2018.
  9. Joint Label Inference in Networks,
    by D. Chakrabarti, S. Funiak, J. Chang, and S. A. Macskassy, invited to the WWW Journal Track, 2018.
  10. On Mixed Memberships and Symmetric Nonnegative Matrix Factorizations,
    by X. Mao, P. Sarkar, and D. Chakrabarti, in ICML 2017.
  11. Discovery of Topical Authorities in Instagram,
    by A. Pal, A. Herdagdelen, S. Chatterji, S. Taank, and D. Chakrabarti, in WWW 2016.
  12. The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels,
    by P. Sarkar, D. Chakrabarti, and P. Bickel, in NIPS 2015.
  13. Joint Inference of Multiple Label Types in Large Networks,
    by D. Chakrabarti, S. Funiak, J. Chang, and S. A. Macskassy, in ICML 2014.
  14. Speeding up Large-Scale Learning with a Social Prior,
    by D. Chakrabarti, and R. Herbrich, in KDD 2013.
  15. Nonparametric Link Prediction in Dynamic Networks,
    by P. Sarkar, D. Chakrabarti, and M. Jordan, in ICML 2012.
  16. Traffic Shaping to Optimize Ad Delivery,
    by D. Chakrabarti, and E. Vee, in EC 2012.
    Invited to ACM Transactions on Economics and Computation.
  17. Threshold Conditions for Arbitrary Cascade Models on Arbitrary Networks,
    by B. Aditya Prakash, D. Chakrabarti, M. Faloutsos, N. Valler, and C. Faloutsos, in ICDM 2011.
    Invited to KAIS Journal Special Issue (ICDM Best Papers).
  18. Preserving Pairwise Relationships in Subgraphs,
    by A. Vattani, M. Gurevich, and D. Chakrabarti, in ICML 2011.
  19. Event Summarization using Tweets,
    by D. Chakrabarti, and K. Punera, in ICWSM 2011.
  20. Theoretical Justification of Popular Link Prediction Heuristics,
    by P. Sarkar, D. Chakrabarti, and A. W. Moore.
    Invited to IJCAI 2011 (best paper track).
    The original version of this paper was published in COLT 2010 (best student paper award).
  21. Theoretical Justification of Popular Link Prediction Heuristics,
    by P. Sarkar, D. Chakrabarti, and A. W. Moore, in COLT 2010.
    (Best Student Paper Award).
    A more accessible version was published in IJCAI 2011 (best paper track).
  22. The Paths More Taken: Matching DOM Trees to Search Logs for Accurate Webpage Clustering,
    by D. Chakrabarti, and R. Mehta, in WWW 2010.
  23. Mining Broad Latent Query Aspects from Search Sessions,
    by X. Wang, D. Chakrabarti, and K. Punera, in KDD 2009.
  24. Quicklink Selection for Navigational Query Results,
    by D. Chakrabarti, R. Kumar, K. Punera, in WWW 2009.
  25. ShatterPlots: Fast Tools for Mining Large Graphs,
    by A. P. Appel, D. Chakrabarti, C. Faloutsos, R. Kumar, J. Leskovec, and A. Tomkins, in SDM, 2009.
  26. Mortal Multi-Armed Bandits,
    by D. Chakrabarti, R. Kumar, F. Radlinski, and E. Upfal, in NIPS 2008.
  27. Generating Succinct Titles for Web URLs,
    by D. Chakrabarti, R. Kumar, and K. Punera, in KDD 2008.
  28. A Graph-Theoretic Approach to Webpage Segmentation,
    by D. Chakrabarti, R. Kumar, and K. Punera, in WWW 2008.
  29. Contextual Advertising by Combining Relevance with Click Feedback,
    by D. Chakrabarti, D. Agarwal, and V. Josifovski, in WWW 2008.
  30. Estimating Rates of Rare Events at Multiple Resolutions,
    by D. Agarwal, A. Broder, D. Chakrabarti, D. Diklic, V. Josifovski, and M. Sayyadian, in KDD 2007.
  31. Multi-armed Bandit Problems with Dependent Arms,
    by S. Pandey, D. Chakrabarti, and D. Agarwal, in ICML 2007.
  32. Page-level Template Detection via Isotonic Smoothing,
    by D. Chakrabarti, R. Kumar, and K. Punera, in WWW 2007.
  33. Bandits for Taxonomies: A Model-based Approach,
    by S. Pandey, D. Agarwal, D. Chakrabarti, and V. Josifovski, in SDM 2007.
  34. Information Survival Threshold in Sensor and P2P Networks,
    by J. Leskovec, D. Chakrabarti, C. Faloutsos, S. Madden, C. Guestrin, and M. Faloutsos, in IEEE INFOCOM 2007.
  35. Evolutionary Clustering,
    by D. Chakrabarti, Ravi Kumar and A. Tomkins, in KDD 2006.
  36. Neighborhood Formation and Anomaly Detection in Bipartite Graphs,
    by J. Sun, H. Qu, D. Chakrabarti, and C. Faloutsos, in ICDM 2005.
  37. Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication,
    by J. Leskovec, D. Chakrabarti, J. Kleinberg, and C. Faloutsos, in PKDD 2005.
    Awarded the ECML/PKDD Test of Time Prize in 2015.
  38. AutoPart: Parameter-Free Graph Partitioning and Outlier Detection,
    by D. Chakrabarti, in PKDD 2004.
  39. Fully Automatic Cross-Associations,
    by D. Chakrabarti, S. Papadimitriou, D. Modha and C. Faloutsos, in KDD 2004.
  40. R-MAT: A Recursive Model for Graph Mining,
    by D. Chakrabarti, Y. Zhan and C. Faloutsos, in SIAM Data Mining 2004.
    This is the basis for the Graph500 supercomputer benchmark.
  41. NetMine: New Mining Tools for Large Graphs,
    by D. Chakrabarti, Y. Zhan, D. Blandford, C. Faloutsos and G. Blelloch, in the SDM 2004 Workshop on Link Analysis, Counter-terrorism and Privacy.
  42. Epidemic Spreading in Real Networks: An Eigenvalue Viewpoint,
    by Y. Wang, D. Chakrabarti, C. Wang and C. Faloutsos, in SRDS 2003.
  43. F4: Large Scale Automated Forecasting using Fractals,
    by D. Chakrabarti and C. Faloutsos, in CIKM 2002.
  44. Using EM to Learn 3D Models of Indoor Environments with Mobile Robots,
    by Y. Liu, R. Emery, D. Chakrabarti, W. Burgard and S. Thrun, in ICML 2001.
  45. A Method for Acquiring Multi-Planar Volumetric Models with Mobile Robots based on the EM Algorithm,
    by S. Thrun, W. Burgard, D. Chakrabarti, R. Emery, and Y. Liu, in ISRR 2001.
  1. Incentive-Aware Models of Financial Networks,
    by A. Jalan, D. Chakrabarti, and P. Sarkar, in Operations Research (forthcoming).
  2. Robust Linear Classification from Limited Training Data,
    by D. Chakrabarti, in the Machine Learning Journal, 2022, volume 111, issue 5, pages 1621-1649.
  3. Parameter-free Robust Optimization for the Maximum-Sharpe Portfolio Problem,
    by D. Chakrabarti, in the European Journal of Operational Research (EJOR), 2021, volume 293, issue 1, pages 388-399.
  4. Estimating Mixed Memberships with Sharp Eigenvector Deviations,
    by X. Mao, P. Sarkar, and D. Chakrabarti, in the Journal of the American Statistical Association (JASA), 2021, volume 116, issue 536, pages 1928-1940.
  5. Portfolio Construction by Mitigating Error Amplification: The Bounded-Noise Portfolio,
    by L. Zhao, D. Chakrabarti, and K. Muthuraman, in Operations Research, 2019, volume 67, number 4, pages 965-983.
  6. Modeling Node Incentives in Directed Networks,
    by D. Chakrabarti, in the Annals of Applied Statistics, 2017, volume 11, number 4, pages 2298-2331.
  7. Joint Label Inference in Networks,
    by D. Chakrabarti, S. Funiak, J. Chang, and S. A. Macskassy, in JMLR, 2017, volume 18, number 59, pages 1-39.
    Invited to WWW Journal Track, 2018.
  8. Traffic Shaping to Optimize Ad Delivery,
    by D. Chakrabarti, and E. Vee, in ACM Transactions on Economics and Computation, 2015, volume 3, number 2.
  9. Nonparametric Link Prediction in Large Scale Dynamic Networks,
    by P. Sarkar, D. Chakrabarti, and M. Jordan, in the Electronic Journal of Statistics, 2014, volume 8, number 2, pages 2022-2065.
  10. Threshold conditions for arbitrary cascade models on arbitrary networks,
    by B. A. Prakash, D. Chakrabarti, N. Valler, M. Faloutsos, and C. Faloutsos, in Knowledge and Information Systems, 2012, volume 33, issue 3.
  11. Kronecker Graphs: An Approach to Modeling Networks,
    by J. Leskovec, D. Chakrabarti, J. Kleinberg, C. Faloutsos, and Z. Ghahramani, in JMLR, 2010, volume 11 (Feb), pages 985-1042.
  12. Epidemic Thresholds in Real Networks,
    by D. Chakrabarti, Y. Wang, C. Wang, J. Leskovec, and C. Faloutsos, in ACM TISSEC, 2008, 10(4).
  13. Visualization of Large Networks with Min-cut Plots, A-plots and R-MAT,
    by D. Chakrabarti, C. Faloutsos and Y. Zhan, in the International Journal of Human-Computer Studies, 65(5), May 2007.
  14. Graph Mining: Laws, Generators and Algorithms,
    by D. Chakrabarti and C. Faloutsos, in ACM Computing Surveys, 38(1), 2006.
  15. Relevance Search and Anomaly Detection in Bipartite Graphs,
    by J. Sun, H. Qu, D. Chakrabarti, and C. Faloutsos, in SIGKDD Explorations 7(2), 2005.
  16. A Real-Time Expectation Maximization Algorithm for Acquiring Multi-Planar Maps of Indoor Environments with Mobile Robots,
    by S. Thrun, C. Martin, Y. Liu, D. Hahnel, R. Emery-Montemerlo, D. Chakrabarti, and W. Burgard, in IEEE Transactions on Robotics and Automation, 20 (3), pp. 433-442, 2004.
  1. Graph Mining: Laws, Tools, and Case Studies,
    by D. Chakrabarti, and C. Faloutsos,
    published by Morgan Claypool in 2012.
  2. Graph Mining,
    by D. Chakrabarti,
    in the Encyclopedia of Machine Learning, 2010, Part 8.
  3. Graph Mining: Laws and Generators,
    by D. Chakrabarti, C. Faloutsos, and M. McGlohon,
    in Managing and Mining Graph Data, 2010.
  4. Graph Patterns and the R-MAT Generator,
    by D. Chakrabarti, and C. Faloutsos,
    in Mining Graph Data,
    edited by L. Holder and D. Cook, published by Wiley in 2006.

Thesis

Tools for Large Graph Mining
Advisor: Dr. Christos Faloutsos
Institution: School of Computer Science, Carnegie Mellon University
Date: June, 2005

Pre-prints and Tech Reports

  1. Strategic Contract Negotiation in Financial Networks,
    by A. Jalan, and D. Chakrabarti, in 2024.
  2. Incentive-Aware Models of Dynamic Financial Networks,
    by A. Jalan, D. Chakrabarti, and P. Sarkar, in 2022.
  3. Unified Classical and Robust Optimization for Least Squares,
    by L. Zhao, D. Chakrabarti, and K. Muthuraman, in 2018.
  4. Overlapping Clustering Models, and One (class) SVM to Bind Them All,
    by X. Mao, P. Sarkar, and D. Chakrabarti, in 2018.
  5. Estimating Mixed Memberships with Sharp Eigenvector Deviations,
    by X. Mao, P. Sarkar, and D. Chakrabarti, in 2017.
  6. Portfolio Construction by Mitigating Error Amplification: The Bounded-Noise Portfolio,
    by L. Zhao, D. Chakrabarti, and K. Muthuraman, in 2017.
  7. Joint Inference of Multiple Label Types in Large Networks,
    by D. Chakrabarti, S. Funiak, J. Chang, and S. A. Macskassy, in 2014.
  8. Non-parametric Link Prediction,
    by P. Sarkar, D. Chakrabarti, and M. Jordan, in 2013.
  9. ShatterPlots: Fast Tools for Mining Large Graphs,
    by A. P. Appel, D. Chakrabarti, C. Faloutsos, R. Kumar, J. Leskovec, and A. Tomkins, in 2008: CMU-ML-08-116.
  10. Fully Automatic Cross-Associations,
    by D. Chakrabarti, S. Papadimitriou, D. S. Modha and C. Faloutsos, in 2004: CMU-CALD-04-107.
  11. Large-scale Automated Forecasting using Fractals,
    by D. Chakrabarti, in 2002: CMU-CALD-02-101.

Talks and Tutorials

  1. On Scalable Estimation for Overlapping Clustering Models,
    in Joint Statistical Meetings (JSM), 2021.
  2. Overlapping Clustering, and One (class) SVM to Bind Them All,
    in INDSTATS, 2019.
  3. Nonparametric Link Prediction in Dynamic Graphs,
    in the Purdue Statistics Symposium, 2012.
  4. Theoretical and Statistical Formulations of Link Prediction,
    by D. Chakrabarti, in the Graph Exploitation Symposium at MIT Lincoln Lab, 2012.
  5. A Theoretical Justification of Link Prediction Heuristics,
    in MLG 2012.
  6. Statistical Challenges in Computational Advertising,
    by D. Chakrabarti and D. Agarwal, half-day tutorial in KDD 2009.
  7. Algorithmic Challenges in Computational Advertising,
    by D. Chakrabarti and D. Agarwal, half-day tutorial in CIKM 2008.
  8. Clustering Applications at Yahoo!,
    by D. Chakrabarti, in the NIPS 2009 Workshop on Clustering.