@inproceedings{e606af0f11954d7ca87ddc48e4258402,
title = "Sequential Estimation of Network Cascades",
abstract = "We consider the problem of locating the source of a network cascade, given a noisy time-series of network data. Initially, the cascade starts with one unknown, affected vertex and spreads deterministically at each time step. The goal is to find an adaptive procedure that outputs an estimate for the source as fast as possible, subject to a bound on the estimation error. For a general class of graphs, we describe a family of matrix sequential probability ratio tests (MSPRTs) that are first-order asymptotically optimal up to a constant factor as the estimation error tends to zero. We apply our results to lattices and regular trees, and show that MSPRTs are asymptotically optimal for regular trees. We support our theoretical results with simulations.",
keywords = "Network cascade, asymptotic optimality, hypothesis testing, sequential estimation",
author = "Anirudh Sridhar and Poor, {H. Vincent}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 ; Conference date: 01-11-2020 Through 05-11-2020",
year = "2020",
month = nov,
day = "1",
doi = "10.1109/IEEECONF51394.2020.9443409",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "1507--1511",
editor = "Matthews, {Michael B.}",
booktitle = "Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020",
address = "United States",
}