Browsing by Author "Khan, Maleq"

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    Effect of modelling slum populations on influenza spread in Delhi
    Chen, Jiangzhuo; Chu, Shuyu; Chungbaek, Youngyun; Khan, Maleq; Kuhlman, Christopher J.; Marathe, Achla; Mortveit, Henning; Vullikanti, Anil; Xie, Dawen (BMJ, 2016-01-01)
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    Forecasting the Flu: Designing Social Network Sensors for Epidemics
    Shao, Huijuan; Hossain, K.S.M. Tozammel; Wu, Hao; Khan, Maleq; Vullikanti, Anil Kumar S.; Prakash, B. Aditya; Marathe, Madhav V.; Ramakrishnan, Naren (Virginia Tech, 2016-03-08)
    Early detection and modeling of a contagious epidemic can provide important guidance about quelling the contagion, controlling its spread, or the effective design of countermeasures. A topic of recent interest has been to design social network sensors, i.e., identifying a small set of people who can be monitored to provide insight into the emergence of an epidemic in a larger population. We formally pose the problem of designing social network sensors for flu epidemics and identify two different objectives that could be targeted in such sensor design problems. Using the graph theoretic notion of dominators we develop an efficient and effective heuristic for forecasting epidemics at lead time. Using six city-scale datasets generated by extensive microscopic epidemiological simulations involving millions of individuals, we illustrate the practical applicability of our methods and show significant benefits (up to twenty-two days more lead time) compared to other competitors. Most importantly, we demonstrate the use of surrogates or proxies for policy makers for designing social network sensors that require from nonintrusive knowledge of people to more information on the relationship among people. The results show that the more intrusive information we obtain, the longer lead time to predict the flu outbreak up to nine days.
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    Parallel Algorithms for Switching Edges and Generating Random Graphs from Given Degree Sequences using HPC Platforms
    Bhuiyan, Md Hasanuzzaman (Virginia Tech, 2017-11-09)
    Networks (or graphs) are an effective abstraction for representing many real-world complex systems. Analyzing various structural properties of and dynamics on such networks reveal valuable insights about the behavior of such systems. In today's data-rich world, we are deluged by the massive amount of heterogeneous data from various sources, such as the web, infrastructure, and online social media. Analyzing this huge amount of data may take a prohibitively long time and even may not fit into the main memory of a single processing unit, thus motivating the necessity of efficient parallel algorithms in various high-performance computing (HPC) platforms. In this dissertation, we present distributed and shared memory parallel algorithms for some important network analytic problems. First, we present distributed memory parallel algorithms for switching edges in a network. Edge switch is an operation on a network, where two edges are selected randomly, and one of their end vertices are swapped with each other. This operation is repeated either a given number of times or until a specified criterion is satisfied. It has diverse real-world applications such as in generating simple random networks with a given degree sequence and in modeling and studying various dynamic networks. One of the steps in our edge switch algorithm requires generating multinomial random variables in parallel. We also present the first non-trivial parallel algorithm for generating multinomial random variables. Next, we present efficient algorithms for assortative edge switch in a labeled network. Assuming each vertex has a label, an assortative edge switch operation imposes an extra constraint, i.e., two edges are randomly selected and one of their end vertices are swapped with each other if the labels of the end vertices of the edges remain the same as before. It can be used to study the effect of the network structural properties on dynamics over a network. Although the problem of assortative edge switch seems to be similar to that of (regular) edge switch, the constraint on the vertex labels in assortative edge switch leads to a new difficulty, which needs to be addressed by an entirely new algorithmic approach. We first present a novel sequential algorithm for assortative edge switch; then we present an efficient distributed memory parallel algorithm based on our sequential algorithm. Finally, we present efficient shared memory parallel algorithms for generating random networks with exact given degree sequence using a direct graph construction method, which involves computing a candidate list for creating an edge incident on a vertex using the Erdos-Gallai characterization and then randomly creating the edges from the candidates.