Spike-based graph centrality measures

Kathleen Hamilton; Tiffany Mintz; Prasanna Date; Catherine D. Schuman

doi:10.1145/3407197.3407199

Spike-based graph centrality measures

Kathleen Hamilton, Tiffany Mintz, Prasanna Date, Catherine D. Schuman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

16 Scopus citations

Abstract

We derive several spike-based routines that compute or establish bounds on radial centrality measures for undirected graphs and trees without the use of matrix multiplication. These spike-based centrality measures utilize a direct embedding of graph nodes and edges into neurons and synapses, can be implemented with static synapses or plastic synapses, and rely on minimal post-processing of spike rasters. This work contributes to the growing set of graphical applications for neuromorphic hardware.

Original language	English
Title of host publication	ICONS 2020 - Proceedings of International Conference on Neuromorphic Systems 2020
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450388511
DOIs	https://doi.org/10.1145/3407197.3407199
State	Published - Jul 28 2020
Event	2020 International Conference on Neuromorphic Systems, ICONS 2020 - Virtual, Online, United States Duration: Jul 28 2020 → Jul 30 2020

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2020 International Conference on Neuromorphic Systems, ICONS 2020
Country/Territory	United States
City	Virtual, Online
Period	07/28/20 → 07/30/20

Funding

Research sponsored in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract number DE-AC05-00OR22725. Notice: This manuscript has been authored in part by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

graph algorithms
neuromorphic applications
spiking neural networks

Access to Document

10.1145/3407197.3407199

Cite this

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title = "Spike-based graph centrality measures",

abstract = "We derive several spike-based routines that compute or establish bounds on radial centrality measures for undirected graphs and trees without the use of matrix multiplication. These spike-based centrality measures utilize a direct embedding of graph nodes and edges into neurons and synapses, can be implemented with static synapses or plastic synapses, and rely on minimal post-processing of spike rasters. This work contributes to the growing set of graphical applications for neuromorphic hardware.",

keywords = "graph algorithms, neuromorphic applications, spiking neural networks",

author = "Kathleen Hamilton and Tiffany Mintz and Prasanna Date and Schuman, {Catherine D.}",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 2020 International Conference on Neuromorphic Systems, ICONS 2020 ; Conference date: 28-07-2020 Through 30-07-2020",

year = "2020",

month = jul,

day = "28",

doi = "10.1145/3407197.3407199",

language = "English",

series = "ACM International Conference Proceeding Series",

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Hamilton, K, Mintz, T, Date, P & Schuman, CD 2020, Spike-based graph centrality measures. in ICONS 2020 - Proceedings of International Conference on Neuromorphic Systems 2020., 3407199, ACM International Conference Proceeding Series, Association for Computing Machinery, 2020 International Conference on Neuromorphic Systems, ICONS 2020, Virtual, Online, United States, 07/28/20. https://doi.org/10.1145/3407197.3407199

Spike-based graph centrality measures. / Hamilton, Kathleen; Mintz, Tiffany; Date, Prasanna et al.
ICONS 2020 - Proceedings of International Conference on Neuromorphic Systems 2020. Association for Computing Machinery, 2020. 3407199 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - We derive several spike-based routines that compute or establish bounds on radial centrality measures for undirected graphs and trees without the use of matrix multiplication. These spike-based centrality measures utilize a direct embedding of graph nodes and edges into neurons and synapses, can be implemented with static synapses or plastic synapses, and rely on minimal post-processing of spike rasters. This work contributes to the growing set of graphical applications for neuromorphic hardware.

AB - We derive several spike-based routines that compute or establish bounds on radial centrality measures for undirected graphs and trees without the use of matrix multiplication. These spike-based centrality measures utilize a direct embedding of graph nodes and edges into neurons and synapses, can be implemented with static synapses or plastic synapses, and rely on minimal post-processing of spike rasters. This work contributes to the growing set of graphical applications for neuromorphic hardware.

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DO - 10.1145/3407197.3407199

M3 - Conference contribution

AN - SCOPUS:85091520232

T3 - ACM International Conference Proceeding Series

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PB - Association for Computing Machinery

T2 - 2020 International Conference on Neuromorphic Systems, ICONS 2020

Y2 - 28 July 2020 through 30 July 2020

ER -

Spike-based graph centrality measures

Abstract

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Funding

Keywords

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