Title:
A high-performance framework for analyzing massive complex networks

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dc.contributor.advisor Bader, David A.
dc.contributor.author Madduri, Kamesh en_US
dc.contributor.committeeMember Berry, Jonathan
dc.contributor.committeeMember Fujimoto, Richard
dc.contributor.committeeMember Saini, Subhash
dc.contributor.committeeMember Vuduc, Richard
dc.contributor.department Computing en_US
dc.date.accessioned 2008-09-17T19:31:30Z
dc.date.available 2008-09-17T19:31:30Z
dc.date.issued 2008-07-08 en_US
dc.description.abstract Graphs are a fundamental and widely-used abstraction for representing data. We can analytically study interesting aspects of real-world complex systems such as the Internet, social systems, transportation networks, and biological interaction data by modeling them as graphs. Graph-theoretic and combinatorial problems are also pervasive in scientific computing and engineering applications. In this dissertation, we address the problem of analyzing large-scale complex networks that represent interactions between hundreds of thousands to billions of entities. We present SNAP, a new high-performance computational framework for efficiently processing graph-theoretic queries on massive datasets. Graph analysis is computationally very different from traditional scientific computing, and solving massive graph-theoretic problems on current high performance computing systems is challenging due to several reasons. First, real-world graphs are often characterized by a low diameter and unbalanced degree distributions, and are difficult to partition on parallel systems. Second, parallel algorithms for solving graph-theoretic problems are typically memory intensive, and the memory accesses are fine-grained and highly irregular. The primary contributions of this dissertation are the design and implementation of novel parallel graph algorithms for traversal, shortest paths, and centrality computations, optimized for the small-world network topology, and high-performance multithreaded architectures and multicore servers. SNAP (Small-world Network Analysis and Partitioning) is a modular, open-source framework for the exploratory analysis and partitioning of large-scale networks. With SNAP, we demonstrate the capability to process massive graphs with billions of vertices and edges, and achieve up to two orders of magnitude speedup over state-of-the-art network analysis approaches. We also design a new parallel computing benchmark for characterizing the performance of graph-theoretic problems on high-end systems; study data representations for dynamic graph problems on parallel systems; and apply algorithms in SNAP to solve real-world problems in social network analysis and systems biology. en_US
dc.description.degree Ph.D. en_US
dc.identifier.uri http://hdl.handle.net/1853/24712
dc.publisher Georgia Institute of Technology en_US
dc.subject Parallel computing en_US
dc.subject Graph algorithms en_US
dc.subject Multithreaded algorithms en_US
dc.subject Complex networks en_US
dc.subject Graph analysis framework en_US
dc.subject.lcsh Graph theory Data processing
dc.subject.lcsh Network analysis (Planning)
dc.subject.lcsh Combinatorial analysis
dc.subject.lcsh Graph algorithms
dc.title A high-performance framework for analyzing massive complex networks en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.corporatename College of Computing
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
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