My Research Work

    My research areas are big data, graph analytics, high performance computing, parallel and distributed algorithms, randomized algorithms, and data mining.

Click here for a list of my publications.

Click here for a list of talks I gave.

Big Data and Graph Analytics with High-performance Parallel Computing

My work addresses the need for high performance computing in big data analytics with efficient parallel and distributed algorithms and focuses mainly on graph-based analysis of big data. We are swamped with data from a wide range of sources, e.g., the Internet, genomics, proteomics, social network analytics, etc. Graph abstraction and graph-based analysis of data offers valuable insights and can lead to the discoveries of hidden patterns in massive datasets. Traditional data analytic methods and algorithms do not scale well for big data. Furthermore, such huge data may not fit in the memory of a single processing unit, and thus processing such data require distributed-memory systems where the data is distributed among multiple compute nodes. With this background, the big picture of my work is i) generating large-scale graphs representing or modeling a given large-scale dataset, ii) analyze the graphs to gain insights and discover patterns in the datasets, and iii) develop efficient parallel and distributed algorithms for big data analytic problems using HPC systems to be able to deal with large volume of data. I have published a large number of peer-reviewed articles on these topics in top-tier journals and conferences. The papers can be found here. Bellow are some of the specific problems that I have worked on.

Generating Random Graphs

In many cases, complex networks are studied by modeling them by random graphs. Moreover, random graphs are used for modeling dynamic networks to study their evolution over time. Diversity of the complex systems and processes led to the development of various random graph models such as Erdos-Renyi, small-world, preferential attachment, Chung-Lu, exponential random graph (ERGM), recursive matrix (R-MAT), stochastic Kronecker graph, and Block Two-Level Erdos--Renyi (BTER) models. Demand for big random networks necessitates HPC-based efficient algorithms. However, even efficient sequential algorithms for generating such graphs were nonexistent until recently. We are the first to develop efficient MPI-based distributed-memory parallel algorithms for the preferential attachment model and Chung-Lu model. Preferential attachment model generates random graphs with power-law degree distribution, and Chung-Lu model generates graphs following any given degree sequence. Our algorithms scale very well to large number of processors and can generate random graphs with several hundred billion edges in just a few minutes. We evaluate these algorithms rigorously by both theoretical and experimental analysis.

Subgraph Analysis and Mining

Subgraph isomorphism is a canonical problem in several applications, such as social network analysis, data mining, fraud detection and bioinformatics, where we are interested in finding subsets of nodes with specific labels or attributes and mutual relationships in a given graph that match a specific small template graph. In protein-protein interaction networks, frequent subgraphs (referred to as motifs) have been used to characterize the networks and identify functional groups. We have developed two distributed-memory parallel algorithms, called SPARSE and SAHAD, for this problem. SPARSE is an MPI-based algorithm and can work with a template which is composed of two smaller low-diameter sub-templates connected by a single edge. SAHAD is a Hadoop-based algorithm for templates which are in the form of a tree. These algorithms can count occurrences of templates with up to 10 nodes in graphs with several hundred million edges within a few hours.

Counting Triangles in a Graph

A triangle (three vertices with three edges among them) is a special type of subgraph with special significance. Faster algorithms can be developed for triangles in contrast to the algorithms for general subgraphs. Finding the number of triangles in a graph is an important problem in the analysis of complex networks and data mining. Efficient solution to this problem also lead to efficient solutions for several other graph analysis problems, e.g., computation of clustering coefficient, transitivity, triangular connectivity, etc. Although there are a large number of sequential algorithms for counting triangles, there are only a few previous parallel algorithms, which are either shared-memory based or map-reduce based. The map-reduce based algorithms for this problem generate huge volumes of intermediate data. Shuffling and regrouping these intermediate data lead to significant time and memory overhead. In one paper, we present an efficient MPI-based distributed-memory parallel algorithm, called PATRIC, for counting triangles in massive networks. PATRIC scales well to networks with billions of edges and can compute the exact number of triangles in a network with 10 billion edges in 16 minutes. This algorithm employs an overlapping partitioning of the graph. In another paper, we present a distributed-memory parallel algorithm for counting triangles with non-overlapping partitions of the graph leading to the ability of working with even larger graphs. We have also developed a parallel algorithm with dynamic load balancing that helps improve the running time further.

Generating Social Contact Graphs

We have generated urban and national scale social contact graphs using real world data (e.g., census data, travel data, activity survey data, etc.) coupled with behavioral and social theories. We developed synthetic population for the United States and some other countries modeling every individual in the population including household structure, demographics and a 24-hour activity sequence. Then a social contact network is derived from the synthetic population based on physical co-location of interacting persons. These social contact networks are then used for simulating various social diffusion process and studying contagion dynamics.

Studying Graph Properties and Disease Dynamics

We study structural properties of the generated social contact networks and contagious disease propagation in these networks. In an ongoing work, we are studying how various graph properties, such as degree distribution, clustering coefficients, assortativity, subgraph structures, diameter, and shortest path distance affect disease dynamics on a social contact networks. The social network structure plays major role on the epidemic outcome. We show by explicit construction that local properties such as the degree distribution are not sufficient to characterize the global dynamics of reaction-diffusion processes. We introduce a simple edge-flipping step to generate a Markov chain of random graphs with fixed degree and assortativity distributions. We instantiate several such chains, starting from a highly structured, realistic representation of a social contact network and study the global dynamics of a canonical reaction-diffusion process: epidemic spread. We find that the epidemic curves in these graphs are significantly different, and the difference increases as the Markov chain gets further from its starting point. Such insights can prove useful in future epidemiological studies and formulating public health policies.

Distributed Algorithms

We have developed distributed algorithms for some fundamental graph connectivity problems such as the minimum spanning tree, k-connectivity, topology control, leader election, probabilistic tree embedding, and Steiner forest problems. The results form work has been published in more than eight papers in top-tier journal and conferences. In these distributed algorithms, the nature of computation is inherently parallel, and many principles and techniques developed for these algorithms are useful and insightful in the development of HPC-based parallel algorithms for graph-based big data analytics. I also worked on distributed algorithms for wireless sensor networks. To build a data aggregation tree, we developed energy-efficient distributed approximation algorithm for constructing MST in a sensor network. For data aggregation such as sum, min, max, count, and average, it can be shown that MST is the optimal data aggregation tree. We also worked on network monitoring for general communication networks and developed an efficient distributed network monitoring algorithm using edge routers only. This problem becomes easier if we are allowed to use core routers. However, we avoided using busy core router for monitoring purposes to allow the network to operate effectively.