Research Funding

NSF Career Award (Award# IIS1149851, March 12  Feb 17, PI)

NSF REU Award (Award# IIS1659488, June 16  May 19, CoPI)

NSF IIS Small (Award# IIS1909916, October 19  September 23, PI)

eBay eRUPT Award (July 21  June 23, PI)
Our Projects
1. Interactive Pattern Mining on Hidden Data
Mining frequent patterns from a hidden dataset is an important task with various reallife applications. In this research, we propose a solution to this problem that is based on Markov Chain Monte Carlo (MCMC) sampling of frequent patterns.
2. A Generic Framework for Interactive Personalized Interesting Pattern Discovery
In this work, we propose an interactive pattern discovery framework named PRIIME which identifies a set of interesting patterns for a specific user without requiring any prior input on the interestingness measure of patterns from the user. The proposed framework is generic to support discovery of the interesting set, sequence and graph type patterns.
3. Smart Home Exploration Through Interactive Pattern Discovery
In this paper, we introduce a new home discovery tool called RAVEN. It uses interactive feedback over a collection of home featuresets to learn a buyer's interestingness profile. Then it recommends a small list of homes that match with the buyer's interest.
4. An Iterative MapReduce based Frequent Subgraph Mining Algorithm
In this work, we propose a frequent subgraph mining algorithm called FSMH which uses an iterative MapReducebased framework. FSMH is complete as it returns all the frequent subgraphs for a given userdefined support, and it is efficient as it applies all the optimizations that the latest FSM algorithms adopt.
5. Representing Graphs as Bag of Vertices and Partitions for Graph Classification
In this work, we propose a novel approach for solving graph classification using two alternative graph representations, which are the bag of vertices and the bag of partitions. For the first representation, we use deep learning based node features and for the second, we use traditional metric based features.
6. Waiting to be Sold: Prediction of TimeDependent House Selling Probability
In this work, we propose a supervised regression (Cox regression) model inspired by survival analysis to predict the sale probability of a house given historical home sale information within an observation time window.
7. DyLink2Vec: Effective Feature Representation for Link Prediction in Dynamic Networks
A novel method for metric embedding of nodepair instances for a dynamic network. DyLink2Vec models the metric embedding task as an optimal coding problem where the objective is to minimize the reconstruction error, and it solves this optimization task using a gradient descent method
8. GraTFEL: Link Prediction in Dynamic Networks using Graphlet
A novel method for graphlet transitions based feature representation of the nodepair instances. GraTFEL uses unsupervised feature learning methodologies on graphlet transition based features to give a lowdimensional feature representation of the nodepair instances.
9. GRAFT: an Approximate Graphlet Counting Algorithm for Large Graph Analysis
A simple, yet powerful algorithm that obtains the approximate graphlet frequency for all graphlets that have upto 5 vertices.
10. GUISE: A Uniform Sampler for Constructing Frequency Histogram of Graphlets
A Uniform Sampler for Constructing Frequency Histogram of Graphlets. GUISE uses Markov Chain Monte Carlo (MCMC) sampling method for constructing the approximate GFD of a large network.
11. Approximate triangle counting algorithms on Multicores
An approximate triangle counting algorithm, that runs on multicore computers through a multithreaded implementation.
12. Sampling triples from restricted networks using MCMC strategy
Two Indirect triple sampling methods based on Markov Chain Monte Carlo (MCMC) sampling strategy. TripleMCMC samples triple by performing MCMC walk on an imaginary triple sample space. VertexMCMC samples triple by performing MCMC walk on the original network to sample a node and then samples a triple centered by the selected node.