Christos Faloutsos, Michael Mahoney, Lek-heng Lim, Petros Drineas, Mmds C. Faloutsos, Cmu Scs, ...
• Static & dynamic laws; generators
Mining Broad Latent Query Aspects from Search Sessions (2009)
Xuanhui Wang, Deepayan Chakrabarti, Kunal Punera
Search queries are typically very short, which means they are often underspecified or have senses that the user did not think of. A broad latent query aspect is a set of keywords that succinctly...
Sebastian Thrun, Christian Martin, Yufeng Liu, Dirk Hähnel, Rosemary Emery-montemerlo, Deepayan Chakrabarti, ...
Abstract — This paper presents a real-time algorithm for acquiring compact 3D maps of indoor environments, using a mobile robot equipped with range and imaging sensors. Building on previous work on...
Quicklink Selection for Navigational Query Results (2009)
Deepayan Chakrabarti, Ravi Kumar, Kunal Punera
Quicklinks for a website are navigational shortcuts displayed below the website homepage on a search results page, and that let the users directly jump to selected points inside the website. Since...
Generating Succinct Titles for Web URLs (2009)
Deepayan Chakrabarti, Ravi Kumar, Kunal Punera
How can a search engine automatically provide the best and most appropriate title for a result URL (link-title) so that users will be persuaded to click on the URL? We consider the problem of...
Mortal Multi-Armed Bandits (2009)
Deepayan Chakrabarti, Filip Radlinski, Ravi Kumar, Eli Upfal
We formulate and study a new variant of the k-armed bandit problem, motivated by e-commerce applications. In our model, arms have (stochastic) lifetime after which they expire. In this setting an...
Contextual Advertising by Combining Relevance with Click Feedback ABSTRACT (2008)
Contextual advertising supports much of the Web’s ecosystem today. User experience and revenue (shared by the site publisher ad the ad network) depend on the relevance of the displayed ads to the...
Sharad Mehrotra, Christos Faloutsos, Uci C. Faloutsos, Dr. Deepayan Chakrabarti
• Static & dynamic laws; generators
Kronecker Graphs: an approach to modeling networks (2008)
Leskovec, Jure, Chakrabarti, Deepayan, Kleinberg, Jon, Faloutsos, Christos, Gharamani, Zoubin
How can we model networks with a mathematically tractable model that allows for rigorous analysis of network properties? Networks exhibit a long list of surprising properties: heavy tails for the...
Epidemic Thresholds in Real Networks (2008)
Deepayan Chakrabarti, Yang Wang, Chenxi Wang, Jurij Leskovec, Christos Faloutsos
How will a virus propagate in a real network? How long does it take to disinfect a network given particular values of infection rate and virus death rate? What is the single best node to immunize?...
A Graph-Theoretic Approach to Webpage Segmentation (2008)
Deepayan Chakrabarti, Ravi Kumar, Kunal Punera
Categories and Subject Descriptors General Terms
Christos Faloutsos, Hillol Kargupta, Ngdm C. Faloutsos, Ngdm C. Faloutsos, Ngdm C. Faloutsos, Dr. Deepayan Chakrabarti, ...
Data mining: ~ find patterns (rules, outliers) • Problem#1: How do real graphs look like? • Problem#2: How do they evolve? • Problem#3: How to generate realistic graphs
Visualization of Large Networks with Min-cut Plots, A-plots and R-MAT ⋆,⋆⋆ (2008)
Deepayan Chakrabarti, Christos Faloutsos, Yiping Zhan
What does a ‘normal ’ computer (or social) network look like? How can we spot ‘abnormal ’ sub-networks in the Internet, or web graph? The answer to such questions is vital for outlier...
ABSTRACT Evolutionary Clustering (2008)
Deepayan Chakrabarti, Ravi Kumar, Andrew Tomkins
We consider the problem of clustering data over time. An evolutionary clustering should simultaneously optimize two potentially conflicting criteria: first, the clustering at any point in time should...
We develop a novel framework for the page-level template detection problem. Our framework is built on two main ideas. The first is the automatic generation of training data for a classifier that,...
Sebastian Thrun, Wolfram Burgard, Deepayan Chakrabarti, Rosemary Emery, Yufeng Liu
This paper describes an algorithm for generating compact 3D models of indoor environments with mobile robots. Our algorithm employs the expectation maximization algorithm to fit a low-complexity...
Estimating Rates of Rare Events at Multiple Resolutions ABSTRACT (2008)
Deepak Agarwal, Andrei Broder, Deepayan Chakrabarti, Dejan Diklic, Josifovski Mayssam Sayyadian
We consider the problem of estimating occurrence rates of rare events for extremely sparse data, using pre-existing hierarchies to perform inference at multiple resolutions. In particular, we focus...
Sebastian Thrun, Christian Martin, Yufeng Liu, Dirk Hähnel, Rosemary Emery-montemerlo, Deepayan Chakrabarti, ...
Abstract — This paper presents a real-time algorithm for acquiring compact 3D maps of indoor environments, using a mobile robot equipped with range and imaging sensors. Building on previous work on...
Epidemic Thresholds in Real Networks (2008)
Deepayan Chakrabarti, Yang Wang, Chenxi Wang, Jure Leskovec, Christos Faloutsos
How will a virus propagate in a real network? How long does it take to disinfect a network given particular values of infection rate and virus death rate? What is the single best node to immunize?...
oping An Internet Based Distributed Environment For Collaboration (2007)
Apurva Sharma, Deepayan Chakrabarti, To The, Dr. Pankaj Jalote
Chakrabarti(96089) , has been carried out under my supervision and that this work has not been submitted elsewhere for a degree.
Fully Automatic Cross-Associations (2007)
Chakrabarti, Deepayan, Modha, Dharmendra S., Papadimitriou, Spiros, Fabloutsos, Christos
Large, sparse binary matrices arise in numerous data mining applications, such as the analysis of market baskets, web graphs, social networks, co-citations, as well as information retrieval,...
Bandits for taxonomies: A modelbased approach (2007)
Sandeep Pandey, Deepak Agarwal, Deepayan Chakrabarti, Vanja Josifovski
We consider a novel problem of learning an optimal matching, in an online fashion, between two feature spaces that are organized as taxonomies. We formulate this as a multi-armed bandit problem where...
Multi-armed Bandit Problems with Dependent Arms (2007)
Sandeep Pandey, Deepayan Chakrabarti, Deepak Agarwal
We provide a framework to exploit dependencies among arms in multi-armed bandit problems, when the dependencies are in the form of a generative model on clusters of arms. We find an optimal MDP-based...
Information survival threshold in sensor and P2P networks (2007)
Deepayan Chakrabarti, Jure Leskovec, Christos Faloutsos, Samuel Madden, Carlos Guestrin, Michalis Faloutsos
Abstract—Consider a network of, say, sensors, or P2P nodes, or bluetooth-enabled cell-phones, where nodes transmit information to each other and where links and nodes can go up or down. Consider...
Pagelevel template detection via isotonic smoothing (2007)
We develop a novel framework for the page-level template detection problem. Our framework is built on two main ideas. The first is the automatic generation of training data for a classifier that,...
Bandits for taxonomies: A modelbased approach (2007)
Sandeep Pandey, Deepak Agarwal, Deepayan Chakrabarti, Vanja Josifovski
We consider a novel problem of learning an optimal matching, in an online fashion, between two feature spaces that are organized as taxonomies. We formulate this as a multi-armed bandit problem where...
Pagelevel template detection via isotonic smoothing (2007)
We develop a novel framework for the page-level template detection problem. Our framework is built on two main ideas. The first is the automatic generation of training data for a classifier that,...
Information survival threshold in sensor and P2P networks (2007)
Deepayan Chakrabarti, Jure Leskovec, Christos Faloutsos, Samuel Madden, Carlos Guestrin, Michalis Faloutsos
Abstract—Consider a network of, say, sensors, or P2P nodes, or bluetooth-enabled cell-phones, where nodes transmit information to each other and where links and nodes can go up or down. Consider...
Information survival threshold in sensor and P2P networks (2007)
Deepayan Chakrabarti, Jure Leskovec, Christos Faloutsos, Samuel Madden, Carlos Guestrin, Michalis Faloutsos
Abstract — Consider a network of, say, sensors, or P2P nodes, or bluetooth-enabled cell-phones, where nodes transmit information to each other and where links and nodes can go up or down. Consider...
Information survival threshold in sensor and P2P networks (2007)
Deepayan Chakrabarti, Jure Leskovec, Christos Faloutsos, Samuel Madden, Carlos Guestrin, Michalis Faloutsos
Abstract — Consider a network of, say, sensors, or P2P nodes, or bluetooth-enabled cell-phones, where nodes transmit information to each other and where links and nodes can go up or down. Consider...
Multi-armed Bandit Problems with Dependent Arms (2007)
Sandeep Pandey, Deepayan Chakrabarti, Deepak Agarwal
We provide a framework to exploit dependencies among arms in multi-armed bandit problems, when the dependencies are in the form of a generative model on clusters of arms. We find an optimal MDP-based...
Tools for Large Graph Mining (2006)
Graphs show up in a surprisingly diverse set of disciplines, ranging from computer networks to sociology, biology, ecology and many more. How do such "normal" graphs look like? How can we spot...
Graph mining: Laws, generators, and algorithms (2006)
Deepayan Chakrabarti, Christos Faloutsos
How does the Web look? How could we tell an abnormal social network from a normal one? These and similar questions are important in many fields where the data can intuitively be cast as a graph;...
Graph mining: Laws, generators, and algorithms (2006)
Deepayan Chakrabarti, Christos Faloutsos
How does the Web look? How could we tell an abnormal social network from a normal one? These and similar questions are important in many fields where the data can intuitively be cast as a graph;...
Jurij Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos
Abstract. How can we generate realistic graphs? In addition, how can we do so with a mathematically tractable model that makes it feasible to analyze their properties rigorously? Real graphs obey a...
Neighborhood formation and anomaly detection in bipartite graphs (2005)
Jimeng Sun, Huiming Qu, Deepayan Chakrabarti, Christos Faloutsos
Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific...
Neighborhood formation and anomaly detection in bipartite graphs (2005)
Jimeng Sun, Huiming Qu, Deepayan Chakrabarti, Christos Faloutsos
Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific...
Relevance search and anomaly detection in bipartite graphs (2005)
Jimeng Sun, Huiming Qu, Deepayan Chakrabarti, Christos Faloutsos
Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific...
Neighborhood formation and anomaly detection in bipartite graphs (2005)
Jimeng Sun, Huiming Qu, Deepayan Chakrabarti, Christos Faloutsos
Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific...
Realistic, Mathematically Tractable Graph (2005)
Generation And Evolution, Jurij Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos
How can we generate realistic graphs? In addition, how can we do so with a mathematically tractable model that makes it feasible to analyze their properties rigorously? Real graphs obey a long list...
Tools for Large Graph Mining (2005)
by a gift from Northrop-Grumman Corporation. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either...
Tools for Large Graph Mining (2005)
Deepayan Chakrabarti, Guy Blelloch, Christopher Olston, Jon Kleinberg, External Member
Graphs show up in a surprisingly diverse set of disciplines, ranging from computer networks to sociology, biology, ecology and many more. How do such “normal ” graphs look like? How can we spot...
Relevance search and anomaly detection in bipartite graphs (2005)
Jimeng Sun, Huiming Qu, Deepayan Chakrabarti, Christos Faloutsos
Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific...
Jurij Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos
Abstract. How can we generate realistic graphs? In addition, how can we do so with a mathematically tractable model that makes it feasible to analyze their properties rigorously? Real graphs obey a...
Fully automatic cross-associations (2004)
Deepayan Chakrabarti, Spiros Papadimitriou, Dharmendra S. Modha, Christos Faloutsos
Large, sparse binary matrices arise in numerous data mining applications, such as the analysis of market baskets, web graphs, social networks, co-citations, as well as information retrieval,...
N etM ine: New Mining Tools for Large Graphs (2004)
Deepayan Chakrabarti, Yiping Zhan, Daniel Bl, Christos Faloutsos, Guy Blelloch
Discovering patterns, laws and regularities in large real networks has numerous applications: Analysis of virus propagation patterns, on both social/e-mail as well as physical-contact networks [33];...
R-MAT: A recursive model for graph mining (2004)
Deepayan Chakrabarti, Yiping Zhan, Christos Faloutsos
How does a ‘normal ’ computer (or social) network look like? How can we spot ‘abnormal ’ sub-networks in the Internet, or web graph? The answer to such questions is vital for outlier...
Fully automatic cross-associations (2004)
Deepayan Chakrabarti, Spiros Papadimitriou, Dharmendra S. Modha, Christos Faloutsos
Large, sparse binary matrices arise in numerous data mining applications, such as the analysis of market baskets, web graphs, social networks, co-citations, as well as information retrieval,...
Fully automatic cross-associations (2004)
Deepayan Chakrabarti, Spiros Papadimitriou, Dharmendra S. Modha, Christos Faloutsos
Large, sparse binary matrices arise in numerous data mining applications, such as the analysis of market baskets, web graphs, social networks, co-citations, as well as information retrieval,...
R-MAT: A recursive model for graph mining (2004)
Deepayan Chakrabarti, Yiping Zhan, Christos Faloutsos
How does a ‘normal ’ computer (or social) network look like? How can we spot ‘abnormal ’ sub-networks in the Internet, or web graph? The answer to such questions is vital for outlier...
Deepayan Chakrabarti, Spiros Papadimitriou, Dharmendra S. Modha, Christos Faloutsos
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, or other...
Epidemic Spreading in Real Networks: An Eigenvalue Viewpoint (2003)
Yang Wang, Deepayan Chakrabarti, Chenxi Wang, Christos Faloutsos
Abstract How will a virus propagate in a real network?Does an epidemic threshold exist for a finite powerlaw graph, or any finite graph? How long does ittake to disinfect a network given particular...
Epidemic Spreading in Real Networks: An Eigenvalue Viewpoint (2003)
Yang Wang, Deepayan Chakrabarti, Chenxi Wang, Christos Faloutsos
How will a virus propagate in a real network? Does an epidemic threshold exist for a finite powerlaw graph, or any finite graph? How long does it take to disinfect a network given particular values...
Hot DB’02 Course Project Reports: Hot Topics in Database Systems, Fall 2002 (2003)
Edited Anastassia Ailamaki, Stavros Harizopoulos, Deepayan Chakrabarti, Stavros Harizopoulos, Hyang-ah Kim, Suman Nath, ...
Epidemic Spreading in Real Networks: An Eigenvalue Viewpoint (2003)
Yang Wang, Deepayan Chakrabarti, Christos Faloutsos, Chenxi Wang, Chenxi Wang
How will a virus propagate in a real network? Does an epidemic threshold exist for a nite power-law graph, or any nite graph? How long does it take to disinfect a network given particular values of...
Epidemic Spreading in Real Networks: An Eigenvalue Viewpoint (2003)
Yang Wang, Deepayan Chakrabarti, Chenxi Wang, Christos Faloutsos
How will a virus propagate in a real network? Does an epidemic threshold exist for a finite powerlaw graph, or any finite graph? How long does it take to disinfect a network given particular values...
Large-scale Automated Forecasting using Fractals (2002)
Forecasting has attracted a lot of research interest, with very successful methods for periodic time sequences. Here, we propose a fast, automated method to do non-linear forecasting, for both...
C.: F4: large-scale automated forecasting using fractals (2002)
Forecasting has attracted a lot of research interest, with very successful methods for periodic time series. Here, we propose a fast, automated method to do non-linear forecasting, for both periodic...
F4: Large-Scale Automated Forecasting Using Fractals (2002)
Deepayan Chakrabarti, Christos Faloutsos
Forecasting has attracted a lot of research interest, with very successful methods for periodic time series. Here, we propose a fast, automated method to do non-linear forecasting, for both periodic...
C.: F4: large-scale automated forecasting using fractals (2002)
Forecasting has attracted a lot of research interest, with very successful methods for periodic time series. Here, we propose a fast, automated method to do non-linear forecasting, for both periodic...
C.: F4: large-scale automated forecasting using fractals (2002)
Deepayan Chakrabarti, Christos Faloutsos
Forecasting has attracted a lot of research interest, with very successful methods for periodic time series. Here, we propose a fast, automated method to do non-linear forecasting, for both periodic...
Using EM to learn 3d environment models with mobile robots (2001)
Yufeng Liu, Rosemary Emery, Deepayan Chakrabarti, Wolfram Burgard
Submitted to ICML-01 This paper describes an algorithm for generating compact 3D models of indoor environments with mobile robots. Our algorithm employs the expectation maximization algorithm to fit...
Using EM to Learn 3D Models of Indoor Environments with Mobile Robots (2001)
Yufeng Liu Yufeng, Rosemary Emery, Deepayan Chakrabarti, Wolfram Burgard
This paper describes an algorithm for generating compact 3D models of indoor environments with mobile robots. Our algorithm employs the expectation maximization algorithm to fit a lowcomplexity...