Thesis
|
Tools for Large Graph Mining
[pdf, 5MB],
and a 17-page summary [pdf, 1.7MB]
|
Refereed Publications
|
In
reverse chronological order:
- 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:
pdf
- Preserving Pairwise Relationships in Subgraphs,
by A. Vattani, M. Gurevich, and D. Chakrabarti,
in ICML 2011:
pdf
- Event Summarization using Tweets,
by D. Chakrabarti, and K. Punera,
in ICWSM 2011:
pdf
- Theoretical Justification
of Popular Link Prediction Heuristics,
by P. Sarkar, D. Chakrabarti, and A. W. Moore,
in COLT 2010 (Best Student Paper Award):
pdf and
ppt
A more accessible version was published in IJCAI 2011 (best paper track):
pdf, and
ppt
- The Paths More Taken: Matching DOM Trees to Search Logs for Accurate Webpage Clustering,
by D. Chakrabarti, and R. Mehta,
in WWW 2010:
pdf and
ppt
- 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:
pdf
- Mining Broad Latent Query
Aspects from Search Sessions,
by X. Wang, D. Chakrabarti, and K. Punera,
in KDD 2009:
pdf
- Quicklink Selection for
Navigational Query Results,
by D. Chakrabarti, R. Kumar, K. Punera,
in WWW 2009:
pdf and
ppt
- Mortal Multi-Armed
Bandits,
by D. Chakrabarti, R. Kumar, F. Radlinkski, and E. Upfal,
in NIPS 2008:
pdf and
1-page poster
- Generating Succinct Titles
for Web URLs,
by D. Chakrabarti, R. Kumar, and K. Punera,
in KDD 2008:
pdf and
ppt
- A Graph-Theoretic
Approach to Webpage Segmentation,
by D. Chakrabarti, R. Kumar, and K. Punera,
in WWW 2008:
pdf and
ppt
- Contextual
Advertising by Combining Relevance with Click Feedback,
by D. Chakrabarti, D. Agarwal, and V. Josifovski,
in WWW 2008:
pdf and
ppt (1hr,
30 min)
- Epidemic Thresholds
in Real Networks,
by D. Chakrabarti, Y. Wang, C. Wang, J. Leskovec, and C. Faloutsos,
in ACM TISSEC, 10(4), 2008:
pdf
- 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
- 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:
pdf and
ppt
- Multi-armed
Bandit Problems with Dependent Arms,
by S. Pandey, D. Chakrabarti, and D. Agarwal,
in ICML 2007:
pdf and
ppt
- Page-level Template
Detection via Isotonic Smoothing,
by D. Chakrabarti, R. Kumar, and K. Punera,
in WWW 2007 (pages 61-70), Banff, Canada:
pdf and
ppt
- Bandits
for Taxonomies: A Model-based Approach,
by S. Pandey, D. Agarwal, D. Chakrabarti, and V. Josifovski,
in SDM 2007, Minneapolis, Minnesota:
pdf and
ppt
- 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, Anchorage, Alaska:
pdf
- 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:
pdf
- Graph
Mining: Laws, Generators and Algorithms,
by D. Chakrabarti and C. Faloutsos,
in ACM Computing Surveys, 38(1), 2006:
pdf
- Evolutionary
Clustering,
by D. Chakrabarti, Ravi Kumar and A. Tomkins,
in KDD 2006, Philadelphia, Pennsylvania:
pdf
- Neighborhood
Formation and Anomaly Detection in Bipartite Graphs,
by J. Sun, H. Qu, D. Chakrabarti, and C. Faloutsos,
in ICDM 2005, Houston, Texas:
pdf
- Realistic,
Mathematically Tractable Graph Generation and Evolution, Using
Kronecker Multiplication,
by J. Leskovec, D. Chakrabarti, J. Kleinberg, and C. Faloutsos,
in PKDD 2005, Porto, Portugal:
pdf
- AutoPart:
Parameter-Free Graph Partitioning and Outlier Detection,
by D. Chakrabarti, in PKDD 2004 (pages 112-124), Pisa, Italy:
ps.gz and ppt
- Fully Automatic
Cross-Associations,
by D. Chakrabarti, S. Papadimitriou, D. Modha and C. Faloutsos, in KDD
2004 (pages 79-88), Washington, USA:
pdf and ppt
- R-MAT: A Recursive
Model for Graph Mining,
by D. Chakrabarti, Y. Zhan and C. Faloutsos, in SIAM Data Mining 2004,
Orlando, Florida, USA:
pdf
- 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:
pdf, ps.gz and ppt
- Epidemic Spreading
in Real Networks: An Eigenvalue Viewpoint,
by Y. Wang, D. Chakrabarti, C. Wang and C. Faloutsos, in SRDS 2003
(pages 25-34), Florence, Italy:
pdf, ps.gz and ppt
- 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, 2003:
pdf
- F4: Large Scale
Automated Forecasting using Fractals,
by D. Chakrabarti and C. Faloutsos, in CIKM 2002 (pages 2-9), McLean,
Virginia, USA:
pdf, ps.gz and ppt
- 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 (pages 329-336), Williamstown, MA, USA:
pdf and ps.gz
|
| Invited Talks and Tutorials |
- Statistical Challenges in
Computational Advertising,
by D. Chakrabarti and D. Agarwal, in KDD 2009 and CIKM 2008:
ppt
- Clustering Applications
at Yahoo!,
by D. Chakrabarti, in NIPS 2009 Workshop on Clustering:
ppt
|
| Technical Reports |
- Fully Automatic
Cross-Associations,
by D. Chakrabarti, S. Papadimitriou, D. S. Modha and C. Faloutsos, in
2004: CMU-CALD-04-107:
pdf.gz
- Large-scale
Automated Forecasting using Fractals,
by D. Chakrabarti, in 2002: CMU-CALD-02-101:
pdf
|
| Patents |
- Granted: Customization of
information retrieval through user-supplied code, Patent number 6,611,834, by G.
Aggarwal, D. Chakrabarti, P.
K. Dubey, N. P. Garg, S. Ghosal, A. K. Gupta, A. Kulshreshtha, Ashutosh
and S. K. V. Murthy; assignee IBM Corp.
- Filed: Named as a co-inventor in
13 patents filed by Yahoo! Inc.
|
Software
|
- The CrossAssociations
package for automatically grouping nodes in a large graph
- The NetMine package
for extracting patterns from large graphs
- The F4
non-linear time series forecasting package
|
|
