Graphing trillions of triangles

The increasing size of Big Data is often heralded but how data are transformed and represented is also profoundly important to knowledge discovery, and this is exemplified in Big Graph analytics. Much attention has been placed on the scale of the input graph but the product of a graph algorithm can...

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Bibliographic Details
Published in:Information visualization Vol. 16; no. 3; pp. 157 - 166
Main Author: Burkhardt, Paul
Format: Journal Article
Language:English
Published: London, England SAGE Publications 01.07.2017
SAGE PUBLICATIONS, INC
Subjects:
Algorithms
Arithmetic
Big Data
Counting
Experiments
Exploration
Graphical representations
Graphs
Knowledge discovery
Mathematics
Special Issue: Big Graph Analytics
visual analytics
scalable algorithms
triangle counting
Graph
parallel programming
MapReduce
ISSN:1473-8716, 1473-8724
Online Access:Get full text
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Summary:The increasing size of Big Data is often heralded but how data are transformed and represented is also profoundly important to knowledge discovery, and this is exemplified in Big Graph analytics. Much attention has been placed on the scale of the input graph but the product of a graph algorithm can be many times larger than the input. This is true for many graph problems, such as listing all triangles in a graph. Enabling scalable graph exploration for Big Graphs requires new approaches to algorithms, architectures, and visual analytics. A brief tutorial is given to aid the argument for thoughtful representation of data in the context of graph analysis. Then a new algebraic method to reduce the arithmetic operations in counting and listing triangles in graphs is introduced. Additionally, a scalable triangle listing algorithm in the MapReduce model will be presented followed by a description of the experiments with that algorithm that led to the current largest and fastest triangle listing benchmarks to date. Finally, a method for identifying triangles in new visual graph exploration technologies is proposed.
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ISSN:1473-8716
1473-8724
DOI:10.1177/1473871616666393