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. / Ph. D. / Network analysis has become a popular topic in many disciplines including social sciences, epidemiology, biology, and business as it provides valuable insights about many real-world systems represented as networks. The recent advancement of science and technology has resulted in a massive growth of such networks, and mining and processing such massive networks poses significant challenges, which can be addressed by various high-performance computing (HPC) platforms. In this dissertation, we present parallel algorithms for a few network analytic problems using HPC platforms.
Random networks are widely used for modeling many complex real-world systems such as the Internet, biological, social, and infrastructure networks. Most prior work on generating random graphs involves sequential algorithms, and they can be broadly categorized in two classes: (i) edge switching and (ii) stub-matching. We present parallel algorithms for generating random graphs using both the edge switching and stub-matching methods. Our parallel algorithms for switching edges can generate random networks with billions of edges in a few minutes with 1024 processors. We have studied several load balancing methods to equally distribute workload among the processors to achieve the best performance. The parallel algorithm for generating random graphs using the stub-matching method also shows good speedup for medium-sized networks. We believe the proposed parallel algorithms will prove useful in analyzing and mining of emerging networks.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/80299 |
| Date | 09 November 2017 |
| Creators | Bhuiyan, Md Hasanuzzaman |
| Contributors | Computer Science, Marathe, Madhav Vishnu, Khan, Maleq, Ravi, S. S., Heath, Lenwood S., Vullikanti, Anil Kumar S. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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