HyperHeadTail a Streaming Algorithm for Estimating the Degree Distribution of Dynamic Multigraphs

We introduce HyperHeadTail, a streaming algorithm for estimating the degree distribution of a graph from a stream of edges using very little storage space. Real world graph streams, such as those generated by network traffic or other communication networks, tend to contain repeated elements as well...

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Veröffentlicht in:Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 S. 31 - 39
Hauptverfasser: Stolman, Andrew, Matulef, Kevin
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 31.07.2017
Schriftenreihe:ACM Conferences
Schlagworte:
Computing methodologies
Mathematics of computing
Mathematics of computing > Discrete mathematics
Mathematics of computing > Discrete mathematics > Graph theory
Mathematics of computing > Discrete mathematics > Graph theory > Graph algorithms
Theory of computation
Theory of computation > Design and analysis of algorithms
Theory of computation > Design and analysis of algorithms > Graph algorithms analysis
Theory of computation > Design and analysis of algorithms > Graph algorithms analysis > Dynamic graph algorithms
Theory of computation > Design and analysis of algorithms > Streaming, sublinear and near linear time algorithms
Theory of computation > Design and analysis of algorithms > Streaming, sublinear and near linear time algorithms > Sketching and sampling
Theory of computation > Randomness, geometry and discrete structures
ISBN:1450349935, 9781450349932
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Beschreibung
Zusammenfassung:We introduce HyperHeadTail, a streaming algorithm for estimating the degree distribution of a graph from a stream of edges using very little storage space. Real world graph streams, such as those generated by network traffic or other communication networks, tend to contain repeated elements as well as a temporal nature. Our algorithm handles these situations by extending the HeadTail algorithm of Simpson, Seshadhri, and McGregor [20]. We provide an implementation of HyperHeadTail and demonstrate its utility on both synthetic and real-world data sets. We show that HyperHeadTail offers similar performance to HeadTail, while also providing additional functionality for tracking dynamic graphs that previous algorithms cannot efficiently achieve. We show that with a space usage on the order of 8% of the number of vertices in a graph, we were able to achieve a Relative Hausdorff distance of .27.
ISBN:1450349935
9781450349932
DOI:10.1145/3110025.3119395