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Outward Influence and Cascade Size Estimation in Billion-scale Networks

Outward Influence and Cascade Size Estimation in Billion-scale Networks Outward Influence and Cascade Size Estimation in Billion-scale Networks HUNG T. NGUYEN, TRI P. NGUYEN, Virginia Commonwealth University TAM N. VU, University of Colorado, Boulder & UC Denver THANG N. DINH, Virginia Commonwealth University Estimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new influence measure, termed outward influence (OI), defined as the (expected) number of nodes that a subset of nodes S will activate, excluding the nodes in S. Thus, OI equals, the de facto standard measure, influence spread of S minus |S |. OI is not only more informative for nodes with small influence, but also, critical in designing new effective sampling and statistical estimation methods. Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence spread/outward influence at scale and with rigorous theoretical guarantees. The proposed methods are built on two novel components 1) IICP an important sampling method for outward influence; and 2) RSA, a robust mean estimation method that minimize the number of samples through analyzing variance and range of random variables. Compared to the http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings of the ACM on Measurement and Analysis of Computing Systems Association for Computing Machinery

Outward Influence and Cascade Size Estimation in Billion-scale Networks

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 by ACM Inc.
ISSN
2476-1249
DOI
10.1145/3084457
Publisher site
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Abstract

Outward Influence and Cascade Size Estimation in Billion-scale Networks HUNG T. NGUYEN, TRI P. NGUYEN, Virginia Commonwealth University TAM N. VU, University of Colorado, Boulder & UC Denver THANG N. DINH, Virginia Commonwealth University Estimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new influence measure, termed outward influence (OI), defined as the (expected) number of nodes that a subset of nodes S will activate, excluding the nodes in S. Thus, OI equals, the de facto standard measure, influence spread of S minus |S |. OI is not only more informative for nodes with small influence, but also, critical in designing new effective sampling and statistical estimation methods. Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence spread/outward influence at scale and with rigorous theoretical guarantees. The proposed methods are built on two novel components 1) IICP an important sampling method for outward influence; and 2) RSA, a robust mean estimation method that minimize the number of samples through analyzing variance and range of random variables. Compared to the

Journal

Proceedings of the ACM on Measurement and Analysis of Computing Systems – Association for Computing Machinery

Published: Jun 13, 2017

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