Title
Smoothed Analysis of Dynamic Networks
Abstract
We generalize the technique of smoothed analysis to distributed algorithms in dynamic networks. Whereas standard smoothed analysis studies the impact of small random perturbations of input values on algorithm performance metrics, dynamic graph smoothed analysis studies the impact of random perturbations of the underlying changing network graph topologies. Similar to the original application of smoothed analysis, our goal is to study whether known strong lower bounds in dynamic network models are robust or fragile: do they withstand small random perturbations, or do such deviations push the graphs far enough from a precise pathological instance to enable much better performance? Fragile lower bounds are likely not relevant for real-world deployment, while robust lower bounds represent a true difficulty caused by dynamic behavior. We apply this technique to three standard dynamic network problems with known strong worst-case lower bounds: random walks, flooding, and aggregation. We prove that these bounds provide a spectrum of robustness when subjected to smoothing--some are extremely fragile random walks, some are moderately fragile / robust flooding, and some are extremely robust aggregation.
Year
DOI
Venue
2015
https://doi.org/10.1007/s00446-017-0300-8
Distributed Computing
Keywords
Field
DocType
Smoothed analysis,Dynamic networks,Distributed algorithms,Flooding,Aggregation,Random walks
Edit distance,Dynamic network analysis,Mathematical optimization,Computer science,Random walk,Algorithm,Smoothed analysis,Robustness (computer science),Network topology,Distributed algorithm,Competitive analysis,Distributed computing
Journal
Volume
Issue
ISSN
31
4
0178-2770
Citations 
PageRank 
References 
1
0.36
7
Authors
4
Name
Order
Citations
PageRank
Michael Dinitz118322.53
Jeremy T. Fineman258736.10
Seth Gilbert3141394.72
Calvin C. Newport4126495.49