Michael Hurley
Massachusetts Institute of Technology
14 Papers
126 Citations
Michael Hurley is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Power graph analysis & Computer science. The author has an hindex of 7, co-authored 14 publications.
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Papers
Streaming Graph Challenge: Stochastic Block Partition
Edward K. Kao,Vijay Gadepally,Michael Hurley,Michael Jones,Jeremy Kepner,Sanjeev Mohindra,Paul Monticciolo,Albert Reuther,Siddharth Samsi,William S. Song,Diane Staheli,Steven T. Smith +11 more
TL;DR: This paper describes a graph partition challenge with a baseline partition algorithm of sub-quadratic complexity that employs rigorous Bayesian inferential methods based on a statistical model that captures characteristics of the real-world graphs.
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Static Graph Challenge: Subgraph Isomorphism
Siddharth Samsi,Vijay Gadepally,Michael Hurley,Michael Jones,Edward K. Kao,Sanjeev Mohindra,Paul Monticciolo,Albert Reuther,Steven T. Smith,William S. Song,Diane Staheli,Jeremy Kepner +11 more
TL;DR: The Subgraph Isomorphism Graph Challenge (SIGG) as discussed by the authors is a benchmark for graph analytic systems that can be used to measure and quantitatively compare a wide range of present day and future systems.
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Streaming graph challenge: Stochastic block partition
Edward K. Kao,Vijay Gadepally,Michael Hurley,Michael Jones,Jeremy Kepner,Sanjeev Mohindra,Paul Monticciolo,Albert Reuther,Siddharth Samsi,William S. Song,Diane Staheli,Steven T. Smith +11 more
- 01 Sep 2017
TL;DR: GraphChallenge as discussed by the authors describes a graph partition challenge with a baseline partition algorithm of sub-quadratic complexity, which employs rigorous Bayesian inferential methods based on a statistical model that captures characteristics of real-world graphs.
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GraphChallenge.org: Raising the Bar on Graph Analytic Performance
Siddharth Samsi,Vijay Gadepally,Michael Hurley,Michael Jones,Edward K. Kao,Sanjeev Mohindra,Paul Monticciolo,Albert Reuther,Steven T. Smith,William S. Song,Diane Staheli,Jeremy Kepner +11 more
TL;DR: The recent MIT/Amazon/IEEE Graph Challenge as discussed by the authors has been used to evaluate the performance of graph analysis software, hardware, algorithms, and systems, as well as specific metrics for measuring performance.
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Information theoretic approach for performance evaluation of multi-class assignment systems
TL;DR: In this article, the information-theoretic framework is extended to measure the overall performance of any multiclass assignment system, specifically, any system that can be described using a confusion matrix.