NDLI: Wiggins: Detecting Valuable Information in Dynamic Networks Using Limited Resources

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Wiggins: Detecting Valuable Information in Dynamic Networks Using Limited Resources

Content Provider	ACM Digital Library
Author	Upfal, Eli Mahmoody, Ahmad Riondato, Matteo
Abstract	Detecting new information and events in a dynamic network by probing individual nodes has many practical applications: discovering new webpages, analyzing influence properties in network, and detecting failure propagation in electronic circuits or infections in public drinkable water systems. In practice, it is infeasible for anyone but the owner of the network (if existent) to monitor all nodes at all times. In this work we study the constrained setting when the observer can only probe a small set of nodes at each time step to check whether new pieces of information (items) have reached those nodes. We formally define the problem through an infinite time generating process that places new items in subsets of nodes according to an unknown probability distribution. Items have an exponentially decaying novelty, modeling their decreasing value. The observer uses a probing schedule (i.e., a probability distribution over the set of nodes) to choose, at each time step, a small set of nodes to check for new items. The goal is to compute a schedule that minimizes the average novelty of undetected items. We present an algorithm, WIGGINS, to compute the optimal schedule through convex optimization, and then show how it can be adapted when the parameters of the problem must be learned or change over time. We also present a scalable variant of WIGGINS for the MapReduce framework. The results of our experimental evaluation on real social networks demonstrate the practicality of our approach.
Starting Page	677
Ending Page	686
Page Count	10
File Format	PDF
ISBN	9781450337168
DOI	10.1145/2835776.2835830
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-02-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Rumor catching Social networks Information diffusion
Content Type	Text
Resource Type	Article

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