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Randomized near-neighbor graphs, giant components and applications in data science

Part of: Geometric probability and stochastic geometry Graph theory Special processes

Published online by Cambridge University Press:  16 July 2020

Ariel Jaffe ,
Yuval Kluger ,
George C. Linderman ,
Gal Mishne  and
Stefan Steinerberger
Ariel Jaffe*
Affiliation:
Yale University
Yuval Kluger*
Affiliation:
Yale University
George C. Linderman*
Affiliation:
Yale University
Gal Mishne*
Affiliation:
Yale University
Stefan Steinerberger*
Affiliation:
Yale University
*
*Postal address: Program in Applied Mathematics, Yale University, New Haven, CT 06511, USA. Email address: [email protected]; [email protected]
**Postal address: Department of Pathology and Applied Mathematics, Yale University, New Haven, CT 06511, USA. Email address: [email protected]
*Postal address: Program in Applied Mathematics, Yale University, New Haven, CT 06511, USA. Email address: [email protected]; [email protected]
*Postal address: Program in Applied Mathematics, Yale University, New Haven, CT 06511, USA. Email address: [email protected]; [email protected]
***Postal address: Halicioğlu Data Science Institute, UC San Diego, La Jolla, CA 92093, USA. Email address: [email protected]
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Abstract

If we pick n random points uniformly in $[0,1]^d$ and connect each point to its $c_d \log{n}$ nearest neighbors, where $d\ge 2$ is the dimension and $c_d$ is a constant depending on the dimension, then it is well known that the graph is connected with high probability. We prove that it suffices to connect every point to $ c_{d,1} \log{\log{n}}$ points chosen randomly among its $ c_{d,2} \log{n}$ nearest neighbors to ensure a giant component of size $n - o(n)$ with high probability. This construction yields a much sparser random graph with $\sim n \log\log{n}$ instead of $\sim n \log{n}$ edges that has comparable connectivity properties. This result has non-trivial implications for problems in data science where an affinity matrix is constructed: instead of connecting each point to its k nearest neighbors, one can often pick $k'\ll k$ random points out of the k nearest neighbors and only connect to those without sacrificing quality of results. This approach can simplify and accelerate computation; we illustrate this with experimental results in spectral clustering of large-scale datasets.

Keywords

k-nn graphrandom graphconnectivitysparsification

MSC classification

Primary: 60D05: Geometric probability and stochastic geometry 60K35: Interacting random processes; statistical mechanics type models; percolation theory
Secondary: 05C40: Connectivity 05C80: Random graphs
Type
Research Papers
Information
Journal of Applied Probability , Volume 57 , Issue 2 , June 2020 , pp. 458 - 476
Copyright
© Applied Probability Trust 2020

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