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Conference Paper: SimON-Feedback: An Iterative Algorithm for Performance Tuning in Online Social Simulation

TitleSimON-Feedback: An Iterative Algorithm for Performance Tuning in Online Social Simulation
Authors
Issue Date2019
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001810
Citation
Proceedings of 2019 IEEE International Conference on Intelligence and Security Informatics (ISI), Shenzhen, China, 1-3 July 2019, p. 13-17 How to Cite?
AbstractSimulation of human behaviour being an intrinsically difficult problem, no single algorithm or model can accurately simulate online social networks. One can obtain an optimal and reliable simulation only after combining several models focusing on diverse social aspects. Since all independent models focus on different social aspects, it is inherently difficult to combine and optimize their performance. Moreover blackbox nature of these predictive algorithm makes it difficult to integrate human-guided intelligence. Here we are presenting SimON-Feedback, an iterative ensemble algorithm to combine the prediction of several independent models into a significantly improved simulation of an online social network. To this end, we explore user posting and commenting behavior on Reddit, a large social networking platform comprised of many communities called as subreddits.
Persistent Identifierhttp://hdl.handle.net/10722/278669
ISBN

 

DC FieldValueLanguage
dc.contributor.authorVora, M-
dc.contributor.authorChung, WY-
dc.contributor.authorToraman, C-
dc.contributor.authorHuang, Y-
dc.date.accessioned2019-10-21T02:11:50Z-
dc.date.available2019-10-21T02:11:50Z-
dc.date.issued2019-
dc.identifier.citationProceedings of 2019 IEEE International Conference on Intelligence and Security Informatics (ISI), Shenzhen, China, 1-3 July 2019, p. 13-17-
dc.identifier.isbn978-1-7281-2505-3-
dc.identifier.urihttp://hdl.handle.net/10722/278669-
dc.description.abstractSimulation of human behaviour being an intrinsically difficult problem, no single algorithm or model can accurately simulate online social networks. One can obtain an optimal and reliable simulation only after combining several models focusing on diverse social aspects. Since all independent models focus on different social aspects, it is inherently difficult to combine and optimize their performance. Moreover blackbox nature of these predictive algorithm makes it difficult to integrate human-guided intelligence. Here we are presenting SimON-Feedback, an iterative ensemble algorithm to combine the prediction of several independent models into a significantly improved simulation of an online social network. To this end, we explore user posting and commenting behavior on Reddit, a large social networking platform comprised of many communities called as subreddits.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001810-
dc.relation.ispartofIEEE International Conference on Intelligence and Security Informatics (ISI)-
dc.rightsIEEE International Conference on Intelligence and Security Informatics (ISI). Copyright © IEEE.-
dc.rights©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleSimON-Feedback: An Iterative Algorithm for Performance Tuning in Online Social Simulation-
dc.typeConference_Paper-
dc.identifier.emailChung, WY: wchun@hku.hk-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ISI.2019.8823438-
dc.identifier.scopuseid_2-s2.0-85072947856-
dc.identifier.hkuros307654-
dc.identifier.spage13-
dc.identifier.epage17-
dc.publisher.placeUnited States-

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