Big Data and Scalable Computing Research Group

Md Abdul Motaleb Faysal

PhD Student, Graduate Research Assistant

Naw Safrin Sattar

PhD Student, Graduate Research Assistant

Bikesh Pandey

Undergraduate Researcher

Sanjiv Pradhanang

Research Assistant, COSURP Program

Prakash Joshi

Undergraduate Researcher

Group Members

Currently, the group consists of 2 PhD students and 3 undergraduate students.

The students are working on various problems on large-scale data mining, parallel computing, graph (networks) mining and visualization.

Research Projects

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    Parallel Algorithms for Counting Triangles in Large Graphs

    Keywords: triangle counting, clustering coefficients, distributed-memory algorithms, load balancing, fast and space efficient

    Counting triangles in a network is an important algorithmic problem arising in the study of complex networks. An efficient solution to the triangle counting problem can also lead to efficient solutions for many other graph-theoretic problems, e.g. computation of clustering coefficient, transitivity, and triangular connectivity. Further, triangle counting has important applications in graph analysis. We design efficient parallel algorithms for counting triangles.
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    Parallel Community Detection

    Keywords: community detection, social networks, biological networks, large-scale graphs, louvain algorithm, label-propagation algorithm

    Complex systems are organized in clusters or communities, each having distinct role or function. In the corresponding network representation, each functional unit (community) appears as a tightly-knit set of nodes having a higher connection inside the set than outside. Finding communities may reveal the organization of complex systems and their function. We are currently working on designing parallel scalable algorithms for detecting communities in large-scale networks.

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    Characterizing Social Networks based on Local Neighborhood

    Keywords: local neighborhood, jaccard coefficient, community structure, triangle-dense graphs

    Characterizing real-world social and information networks based on graph-theoretic metrics or properties has been of growing interest. Among the most explored metrics are degree distribution, number of triangles and clustering coefficients. An important property related to triangles, of many networks, is high transitivity, which states that two nodes (vertices) having common neighbor(s) have an elevated probability of being neighbors to one another. We present a characterization of networks based on a quantification of common neighbors.

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    Scalable Mining and Analysis of Protein-Protein Interaction Networks

    Keywords: PPI networks, functional units, scalable framework, disease analysis, drug discovery

    We are working to design scalable algorithmic and analytic techniques to study PPI networks. Our study of PPIs will be based on network-centric mining and analysis approaches. We will design specialized methods for extracting signed motifs, computing centrality, and finding functional units in PPI networks.

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    A Cyber-Infrastructure for Network Science (CINET)

    A web-based system for network-based computation.

    I was a member of CINET project team during my PhD years. This NSF-funded project, titled as "From Desktops to Clouds -- A Middleware for Next Generation Network Science," is a large collaborative research effort. By harnessing new cloud-based resources in an easily accessible manner, network science researchers will be able to deal more complex problems. We have built a cyber infrastructure which is designed to be self-sustainable.

    My role: I worked on designing and implementing highly efficient and scalable algorithms for various problem of network science. The implemented modules serve as computational engine behind the whole system.