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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| Vydané v: | Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 s. 31 - 39 |
|---|---|
| Hlavní autori: | , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
New York, NY, USA
ACM
31.07.2017
|
| Edícia: | ACM Conferences |
| Predmet: |
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
|
| ISBN: | 1450349935, 9781450349932 |
| On-line prístup: | Získať plný text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Matulef, Kevin Stolman, Andrew |
| Author_xml | – sequence: 1 givenname: Andrew surname: Stolman fullname: Stolman, Andrew email: astolman@ucsc.edu organization: University of California, Santa Cruz, Santa Cruz, CA – sequence: 2 givenname: Kevin surname: Matulef fullname: Matulef, Kevin email: kevin@calyxhealth.com organization: Calyx Health Inc., San Francisco, CA |
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| Copyright | 2017 ACM |
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| DOI | 10.1145/3110025.3119395 |
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| Editor | Diesner, Jana Ferrari, Elena Xu, Guandong |
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| PublicationTitle | Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 ASONAM 2017 : proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining : Sydney, Australia, 31 July-03 August, 2017 |
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| Snippet | We introduce HyperHeadTail, a streaming algorithm for estimating the degree distribution of a graph from a stream of edges using very little storage space.... |
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| SubjectTerms | 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 |
| Subtitle | a Streaming Algorithm for Estimating the Degree Distribution of Dynamic Multigraphs |
| Title | HyperHeadTail |
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