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Predictive simulation framework of stochastic diffusion model for identifying top-K influential nodes
conference contribution
posted on 2023-05-23, 08:22 authored by Ohara, K, Saito, K, Kimura, M, Motoda, HWe address a problem of efficiently estimating the influence of a node in information di ffusion over a social network. Since the information di ffusion is a stochastic process, the influence degree of a node is quantified by the expectation, which is usually obtained by very time consuming many runs of simulation. Our contribution is that we proposed a framework for predictive simulation based on the leave-N-out cross validation technique that well approximates the error from the unknown ground truth for two target problems: one to estimate the influence degree of each node, and the other to identify top-K influential nodes. The method we proposed for the first problem estimates the approximation error of the influence degree of each node, and the method for the second problem estimates the precision of the derived top-K nodes, both without knowing the true influence degree. We experimentally evaluate the proposed methods using the three real world networks, and show that they can serve as a good measure to solve the target problems with far fewer runs of simulation ensuring the accuracy if N is appropriately chosen, and that estimating the top-K nodes is easier than estimating the influence degree, which means one can identify the influential nodes without knowing exactly their influence degree.
History
Publication title
Proceedings of the Fifth Asian Conference on Machine Learning 2013Volume
29Editors
CS Ong and TB HoPagination
149-164ISSN
1532-4435Department/School
School of Information and Communication TechnologyPublisher
Microtome PublishingPlace of publication
Brookline, MA USAEvent title
Fifth Asian Conference on Machine Learning 2013Event Venue
Canberra, AustraliaDate of Event (Start Date)
2013-11-13Date of Event (End Date)
2013-11-15Rights statement
Copyright 2013 K. Ohara, K. Saito, M. Kimura & H. MotodaRepository Status
- Restricted