Table of Contents

Preface; Part I. Algorithms: 1. Graphs and matrices; 2. Linear algebraic notation and definitions; 3. Connected components and minimum paths; 4. Some graph algorithms in an array-based language; 5. Fundamental graph algorithms; 6. Complex graph algorithms; 7. Multilinear algebra for analyzing data with multiple linkages; 8. Subgraph detection; Part II. Data: 9. Kronecker graphs; 10. The Kronecker theory of power law graphs; 11. Visualizing large Kronecker graphs; Part III. Computation: 12. Large-scale network analysis; 13. Implementing sparse matrices for graph algorithms; 14. New ideas in sparse matrix-matrix multiplication; 15. Parallel mapping of sparse computations; 16. Fundamental questions in the analysis of large graphs; Index.

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An introduction to graph algorithms accessible to those without a computer science background.

About the Author

Jeremy Kepner is a senior technical staff member at the Massachusetts Institute of Technology Lincoln Laboratory. His research focuses on the development of advanced libraries for the application of massively parallel computing to a variety of data intensive signal processing problems on which he has published many articles. John Gilbert is a SIAM Fellow and Professor of Computer Science at the University of California, Santa Barbara. His research interests are in combinatorial scientific computing, high-performance graph algorithms, tools and software for computational science and engineering, numerical linear algebra and distributed sensing and control.