Paper Info
Title
Query Answering Efficiency in Expert Networks Under Decentralized Search
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(log^2 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 is 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. To the best of our knowledge, this is the first work studying fundamental behaviors of decentralized search in expert networks. The developed performance bounds, confirmed by real datasets, are able to assist in predicting network performance and designing complex expert networks.
Year
DOI
Venue
2016
10.1145/2983323.2983652
ACM International Conference on Information and Knowledge Management
Keywords
Field
DocType
expert networks,query processing,decentralized search,network models,performance bounds,fundamental theories
Query optimization,Data mining,Binary logarithm,Global structure,Information retrieval,Query expansion,Computer science,Web query classification,Network performance,Network conditions,Scalability
Conference
Citations 
PageRank 
References 
1
0.35
8
Authors
6
Name
Order
Citations
PageRank
Liang Ma11048.75
Mudhakar Srivatsa2108477.97
Derya Cansever3294.67
Xifeng Yan46633280.06
Sue E. Kase5105.79
Michelle Vanni65110.16