Performance Bounds of Decentralized Search in Expert Networks for Query Answering
Authors: 
Liang Ma, Mudhakar Srivatsa, Derya Cansever, Xifeng Yan, Sue Kase, Michelle VanniAuthors Info & Claims
Liang Ma, Mudhakar Srivatsa, Derya Cansever, Xifeng Yan, Sue Kase, Michelle VanniAuthors Info & ClaimsArticle No.: 18, Pages 1 - 23
Abstract
Expert networks are formed by a group of expert-professionals with different specialties to collaboratively resolve specific queries posted to the network. In such networks, when a query reaches an expert who does not have sufficient expertise, this query needs to be routed to other experts for further processing until it is completely solved; therefore, query answering efficiency is sensitive to the underlying query routing mechanism being used. Among all possible query routing mechanisms, decentralized search, operating purely on each expert’s local information without any knowledge of network global structure, represents the most basic and scalable routing mechanism, which is applicable to any network scenarios even in dynamic networks. However, there is still a lack of fundamental understanding of the efficiency of decentralized search in expert networks. In this regard, we investigate decentralized search by quantifying its performance under a variety of network settings. Our key findings reveal the existence of network conditions, under which decentralized search can achieve significantly short query routing paths (i.e., between O(log n) and O(log2 n) hops, n: total number of experts in the network). Based on such theoretical foundation, we further study how the unique properties of decentralized search in expert networks are related to the anecdotal small-world phenomenon. In addition, we demonstrate that decentralized search is robust against estimation errors introduced by misinterpreting the required expertise levels. The developed performance bounds, confirmed by real datasets, are able to assist in predicting network performance and designing complex expert networks.
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Index Terms
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Published In
April 2019
342 pages
ISSN:1556-4681
EISSN:1556-472X
DOI:10.1145/3319626
- Editors:
- Charu Aggarwal,
- Xindong Wu
Copyright © 2019 ACM.
© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 March 2019
Accepted: 01 December 2018
Revised: 01 October 2018
Received: 01 September 2016
Published in TKDD Volume 13, Issue 2
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- ARL Network Science CTA
- Army Research Laboratory and was accomplished under Cooperative
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