Below is a subset of my scholarly publications. You may also want to check my Google Scholar and DBLP profiles.

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In the 3rd IEEE International Conference on Big Data Intelligence and Computing (DataCom), Orlando, FL, 2017.

Conference Paper
Protein-protein interaction (PPI) networks are the
networks of protein complexes formed by biochemical events and
electrostatic forces. PPI networks can be used to study diseases
and discover drugs. The causes of diseases are evident on a
protein interaction level. For instance, an elevation of interaction
edge weights of oncogenes is manifested in cancers. Further, the
majority of approved drugs target a particular PPI, and thus
studying PPI networks is vital to drug discovery.
The availability of large datasets and need for efficient analysis
necessitate the design of scalable methods leveraging modern
high-performance computing (HPC) platforms. In this paper,
we design a lighweight framework on a distributed-memory
parallel system, which includes scalable algorithmic and analytic
techniques to study PPI networks and visualize them. Our
study of PPIs will be based on network-centric mining and
analysis approaches. Since PPI networks are signed (labeled)
and weighted, many existing network mining methods working
on simple unweighted networks will be unsuitable to study PPIs.
Further, the large volume and variety of such data limits the
use of sequential tool or methods. Many existing tools also do
not support a convenient workflow starting from automated
data preprocessing to visualizing results and reports for efficient
extraction of intelligence from large-scale PPI networks. Our
framework support automated analytics based on a large range
of extensible methods for extracting signed motifs, computing
centrality, and finding functional units. We design MPI (Message
Passing Interface) based parallel methods and workflow, which
scale to large networks. The framework is also extensible and
sufficiently generic. To the best of our knowledge, all these
capabilities collectively make our tool novel.

**Bibtex:**

@inproceedings{arifuzzaman-ppi-networks,

author = {Arifuzzaman, S. and Pandey, Bikesh},

title = {Scalable Mining and Analysis of Protein-Protein Interaction Networks},

booktitle = {Proceedings of the 3rd IEEE International Conference on Big Data Intelligence and Computing (DataCom), Orlando, FL, USA},

pages = {1--8},

year = {2017},

month= {November}

}

CoRR abs/1706.05151, pages 1-30, 2017.

Journal/ArXiv
In this paper, we present two efficient MPI-based distributed memory parallel algorithms for counting triangles in big graphs. The first algorithm employs overlapping partitioning and efficient load balancing schemes to provide a very fast parallel algorithm. The algorithm scales well to networks with billions of nodes and can compute the exact number of triangles in a network with 10 billion edges in 16 minutes. The second algorithm divides the network into non-overlapping partitions leading to a space-efficient algorithm. Our results on both artificial and real-world networks demonstrate a significant space saving with this algorithm. We also present a novel approach that reduces communication cost drastically leading the algorithm to both a space- and runtime-efficient algorithm. Further, we demonstrate how our algorithms can be used to list all triangles in a graph and compute clustering coefficients of nodes. Our algorithm can also be adapted to a parallel approximation algorithm using an edge sparsification method.

**Bibtex:**

@article{Arif2017Triangle,

title = {Distributed-Memory Parallel Algorithms for Counting and Listing Triangles in Big Graphs},

author = {Shaikh Arifuzzaman and Maleq Khan and Madhav Marathe},

journal = {CoRR},

volume = {1706},

issue = {05151},

pages = {1--30},

year = {2017}

}

Dept. of Computer Science, Virginia Tech, August 2016.

Dissertation
We design MPI-based distributed-memory parallel algorithms for counting triangles and detecting communities in big networks and present related analysis. The dissertation consists of four parts. In Part I, we devise parallel algorithms for counting and enumerating triangles. The first algorithm employs an overlapping partitioning scheme and novel load-balancing schemes leading to a fast algorithm. We also design a space-efficient algorithm using non-overlapping partitioning and an efficient communication scheme. We then present our third parallel algorithm based on dynamic load balancing. In Part II, we characterize networks by quantifying the number of common neighbors and demonstrate its relationship to community structure of networks. In Part III, we design parallel algorithms for detecting communities in big networks. Finally, in Part IV, we present scalable parallel algorithms for a useful graph preprocessing problem-- converting edge list to adjacency list. We present non-trivial parallelization with efficient HPC-based techniques leading to fast and space-efficient algorithms.

**Bibtex:**

@phdthesis{Arif2016Triangle,

title = {Parallel Mining and Analysis of Triangles and Communities in Big Networks},

school = {Dept. of Computer Science, Virginia Tech},

author = {Shaikh Arifuzzaman},

year = {2016},

month = {August}

}

Parallel Graph Algorithms. Ed. David Bader, Chapman & Hall/ CRC Computational Science, 2015. | **ISBN-10:** 1466573260

Book Chapter
Graph theoretic problems are representative of fundamental kernels in traditional and emerging scientific applications, such as complex network analysis, data mining, and computational biology, as well as applications in national security. Graph abstractions are also extensively used to understand and solve challenging problems in scientific computing. Real-world systems, such as the Internet, telephone networks, social interactions, and transportation networks, are analyzed by modeling them as graphs. To efficiently solve large-scale graph problems, it is necessary to design high performance computing systems and novel parallel algorithms. In this book, some of the world’s most renowned experts explore the latest research and applications in this important area.

In proc. of the 17th IEEE International Conference on High Performance Computing and Communications (HPCC), pages 527–534, 2015.

Conference Paper
In this paper, we present a space-efficient MPI based parallel
algorithm for counting exact number of triangles in massive
networks. The algorithm divides the network into nonoverlapping
partitions. Our results demonstrate up to 25-fold
space saving over the algorithm with overlapping partitions.
This space efficiency allows the algorithm to deal with networks
which are 25 times larger. We present a novel approach that
reduces communication cost drastically (up to 90%) leading to
both a space- and runtime-efficient algorithm. Our adaptation
of a parallel partitioning scheme by computing a novel weight
function adds further to the efficiency of the algorithm.

**Bibtex:**

@inproceedings{arifuzzaman-space-triangle,

author = {Arifuzzaman, S. and Khan, Maleq and Marathe, Madhav},

title = {A Space-efficient Parallel Algorithm for Counting Exact Triangles in Massive Networks},

booktitle = {Proceedings of the 17th IEEE International Conference on High Performance Computing and Communications (HPCC 2015), New York City, USA},

pages = {527--534},

year = {2015},

month= {August}

}

In proc. of 2015 IEEE International Conference on Big Data (BigData), pages 1839–1847, 2015.

Conference Paper
In this paper, we present an efficient MPI-based parallel algorithm for counting triangles in large graph. We consider the case where the main memory of each compute node is large enough to contain the entire graph. We observe that for such a case, computation load can be balanced dynamically and present a dynamic load balancing scheme which improves the performance of the algorithm significantly. Our algorithm demonstrates very good speedups and scales to a large number of processors. The algorithm computes the exact number of triangles in a network with 1 billion edges in 2 minutes with only 100 processors. Our results demonstrate that the algorithm is significantly faster than the related algorithms with static partitioning. In fact, for the real-world networks we experimented on, our algorithm achieves at least 2 times runtime efficiency over the fastest algorithm with static load balancing.

**Bibtex:**

@inproceedings{arifuzzaman_triangle_dynamic,

author = {Arifuzzaman, S. and Khan, M and Marathe, M.},

title = {A Fast Parallel Algorithm for Counting Triangles in Graphs using Dynamic Load Balancing},

booktitle = {Proceedings of 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, USA},

pages = {1839--1847},

year = {2015},

month= {October},

location = {CA, USA}

}

In proc. of the 23rd High Performance Computing Symposium (HPC), pages 17–24, 2015.

Conference Paper
In this paper, we present efficient MPI-based distributed memory parallel algorithms for converting edge lists to adjacency lists. To the best of our knowledge, this is the first work on this problem. To address the critical load balancing issue, we present a parallel load balancing scheme which improves both time and space efficiency significantly. Our fast parallel algorithm works on massive graphs, achieves very good speedups, and scales to large number of processors. The algorithm can convert an edge list of a graph with 20 billion edges to the adjacency list in less than 2 minutes using 1024 processors. Denoting the number of nodes, edges and processors by n, m, and P, respectively, the time complexity of our algorithm is O(m/p + n + P) which provides a speedup factor of at least Ω(min{P, d_avg}), where davg is the average degree of the nodes. The algorithm has a space complexity of O(m/p), which is optimal.

**Bibtex:**

@inproceedings{graph_conversion,

author = {Arifuzzaman, S. and Khan, M.},

title = {Fast Parallel Conversion of Edge List to Adjacency List for Large-scale Graphs},

booktitle = {Proceedings of the 23rd High Performance Computing Symposium (HPC 2015), Alexandria, VA, USA},

pages = {17--24},

year = {2015},

month= {April},

location = {Alexandria, VA, USA}

}

In proc. of the 10th IEEE International Conference on eScience (eScience), pages 324–331, 2014.

Conference Paper
Analysis of structural properties and dynamics of
networks is currently a central topic in many disciplines including
Social Sciences, Biology and Business. CINET, a cyberinfrastructure
for such studies, introduced the concept of supporting
network analysis as a service. The basic idea is to allow experts in
various disciplines to focus on obtaining domain-specific insights
from the results of network analyses instead of worrying about
programming details and allocation of computational resources
needed to carry out the analyses. A basic version of CINET
was released in May 2012. This paper discusses CINET 2.0, a
significantly enhanced version that supports complex network
analyses through a web portal. CINET 2.0 has already been used
for teaching courses related to Network Science at several US
universities. In this paper, we discuss how CINET 2.0 significantly
extends CINET 1.0 through enhancements to some components
and the addition of new components.

**Bibtex:**

@inproceedings{cinet2_2014,

author = {Sherif Hanie El Meligy Abdelhamid and Md. Maksudul Alam and Richard Al{\'{o}} and Shaikh Arifuzzaman and Peter H. Beckman and Tirtha Bhattacharjee and Md Hasanuzzaman Bhuiyan and Keith R. Bisset and Stephen Eubank and Albert C. Esterline and Edward A. Fox and Geoffrey Fox and S. M. Shamimul Hasan and Harshal Hayatnagarkar and Maleq Khan and Chris J. Kuhlman and Madhav V. Marathe and Natarajan Meghanathan and Henning S. Mortveit and Judy Qiu and S. S. Ravi and Zalia Shams and Ongard Sirisaengtaksin and Samarth Swarup and Anil Kumar S. Vullikanti and Tak{-}Lon Wu},

title = {{CINET} 2.0: {A} CyberInfrastructure for Network Science},

booktitle = {Proceedings of the 10th {IEEE} International Conference on e-Science (e-Science 2014), Sao Paulo, Brazil},

pages = {324--331},

year = {2014},

month = {October}

}

CoRR abs/1406.5687, pages 1-10, 2014.

Journal/ArXiv
**Bibtex:**

@article{Arifuzzaman_triangle_14,

author = {Shaikh Arifuzzaman and Maleq Khan and Madhav V. Marathe},

title = {Parallel Algorithms for Counting Triangles in Networks with Large Degrees},

journal = {CoRR},

volume = {abs/1406.5687},

pages = {1--10},

year = {2014}

}

In proc. of the 22nd ACM International Conference on Information and Knowledge Management (CIKM), pages 529–538, 2013.

Conference Paper
In this 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 nodes and can compute the exact number of triangles in a network with one billion nodes and 10 billion edges in 16 minutes. Balancing computational loads among processors for a graph problem like counting triangles is a challenging issue. We present and analyze several schemes for balancing load among processors for the triangle counting problem. These schemes achieve very good load balancing. We also show how our parallel algorithm can adapt an existing edge sparsification technique to approximate the number of triangles with very high accuracy. This modification allows us to count triangles in even larger networks.

**Bibtex:**

@inproceedings{arifuzzaman_triangle_cikm13,

author = {Shaikh Arifuzzaman and Maleq Khan and Madhav V. Marathe},

title = {{PATRIC:} a parallel algorithm for counting triangles in massive networks},

booktitle = {Proceedings of the 22nd {ACM} International Conference on Information and Knowledge Management (CIKM 2013), San Francisco, CA, USA},

pages = {529--538},

year = {2013},

month ={October}

}

In proc. of the 8th IEEE International Conference on eScience (eScience), pages 1–8, 2012.

Conference Paper
Networks are an effective abstraction for representing real systems. Consequently, network science is increasingly used in academia and industry to solve problems in many fields. Computations that determine structure properties and dynamical behaviors of networks are useful because they give insights into the characteristics of real systems. We introduce a newly built and deployed cyberinfrastructure for network science (CINET) that performs such computations, with the following features: (i) it offers realistic networks from the literature and various random and deterministic network generators; (ii) it provides many algorithmic modules and measures to study and characterize networks; (iii) it is designed for efficient execution of complex algorithms on distributed high performance computers so that they scale to large networks; and (iv) it is hosted with web interfaces so that those without direct access to high performance computing resources and those who are not computing experts can still reap the system benefits. It is a combination of application design and cyberinfrastructure that makes these features possible. To our knowledge, these capabilities collectively make CINET novel. We describe the system and illustrative use cases, with a focus on the CINET user.

**Bibtex:**

@inproceedings{cinet_2012,

author = {Sherif Elmeligy Abdelhamid and Richard Al{\'{o}} and S. M. Arifuzzaman and Peter H. Beckman and Md Hasanuzzaman Bhuiyan and Keith R. Bisset and Edward A. Fox and Geoffrey Charles Fox and Kevin Hall and S. M. Shamimul Hasan and Anurodh Joshi and Maleq Khan and Chris J. Kuhlman and Spencer J. Lee and Jonathan Leidig and Hemanth Makkapati and Madhav V. Marathe and Henning S. Mortveit and Judy Qiu and S. S. Ravi and Zalia Shams and Ongard Sirisaengtaksin and Rajesh Subbiah and Samarth Swarup and Nick Trebon and Anil Vullikanti and Zhao Zhao},

title = {{CINET:} {A} cyberinfrastructure for network science},

booktitle = {Proceedings of the 8th {IEEE} International Conference on e-Science (e-Science 2012), Chicago, IL, USA},

pages = {1--8},

year = {2012},

month = {October}

}

In 2012 SC Companion: High Performance Computing, Networking Storage and Analysis (SC12), pages 1448–1449, 2012.

Extended Abstract
We present MPI-based parallel algorithms for counting triangles and computing clustering coefficients in massive networks. Counting triangles is important in the analysis of various networks, e.g., social, biological, web etc. Emerging massive networks do not fit in the main memory of a single machine and are very challenging to work with. Our distributed-memory parallel algorithm allows us to deal with such massive networks in a time- and space-efficient manner. We were able to count triangles in a graph with 2 billions of nodes and 50 billions of edges in 10 minutes. Our parallel algorithm for computing clustering coefficients uses efficient external memory aggregation. We also show how edge sparsification technique can be used with our parallel algorithm to find approximate number of triangles without sacrificing the accuracy of estimation. In addition, we propose a simple modification of a state-of-the-art sequential algorithm that improves both runtime and space requirement.

**Bibtex:**

@inproceedings{extabst_sc12,

author = {S. M. Arifuzzaman and Maleq Khan and Madhav V. Marathe},

title = {Abstract: Parallel Algorithms for Counting Triangles and Computing Clustering Coefficients},

booktitle = {2012 {SC} Companion: High Performance Computing, Networking Storage and Analysis, Salt Lake City, UT, USA},

pages = {1448--1449},

year = {2012},

month = {November}

}