Andrew
Pitts
Springer Nature
setting
graph estimator
concentration
2012-01-01
subgraphs
Counting Arbitrary Subgraphs in Data Streams
number of occurrences
https://doi.org/10.1007/978-3-642-31585-5_53
applicability
estimator
size
2012
problem
constant size
graph H
cases
work
arbitrary subgraphs
turnstile model
model
graph G.
We study the subgraph counting problem in data streams. We provide the first non-trivial estimator for approximately counting the number of occurrences of an arbitrary subgraph H of constant size in a (large) graph G. Our estimator works in the turnstile model, i.e., can handle both edge-insertions and edge-deletions, and is applicable in a distributed setting. Prior to this work, only for a few non-regular graphs estimators were known in case of edge-insertions, leaving the problem of counting general subgraphs in the turnstile model wide open. We further demonstrate the applicability of our estimator by analyzing its concentration for several graphs H and the case where G is a power law graph.
graph
false
occurrence
chapter
2022-12-01T06:46
power-law graphs
streams
data streams
598-609
counting problem
subgraph H
general subgraphs
subgraph counting problem
number
https://scigraph.springernature.com/explorer/license/
chapters
G.
Kurt
Mehlhorn
Sauerwald
Thomas
Automata, Languages, and Programming
978-3-642-31585-5
978-3-642-31584-8
Computation Theory and Mathematics
Max Planck Institute for Informatics, Germany
Institute for Modern Mathematics and Physics, Fudan University, China
Institute for Modern Mathematics and Physics, Fudan University, China
He
Sun
Mehlhorn
Kurt
Springer Nature - SN SciGraph project
Daniel M.
Kane
Max Planck Institute for Informatics, Germany
Max Planck Institute for Informatics, Germany
doi
10.1007/978-3-642-31585-5_53
Artur
Czumaj
pub.1011001481
dimensions_id
Wattenhofer
Roger
Department of Mathematics, Stanford University, USA
Department of Mathematics, Stanford University, USA
Information and Computing Sciences