Learning and Controlling Network Diffusion in Dependent Cascade Models

Diffusion processes have increasingly been used to represent flow of ideas, traffic and diseases in networks. Learning and controlling the diffusion dynamics through management actions has been studied extensively in the context of independent cascade models, where diffusion on outgoing edges from a...

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Main Authors: DU, Jiali, VARAKANTHAM, Pradeep, Akshat KUMAR, CHENG, Shih-Fen
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Language:English
Published: Institutional Knowledge at Singapore Management University 2015
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Online Access:https://ink.library.smu.edu.sg/sis_research/2928
https://ink.library.smu.edu.sg/context/sis_research/article/3928/viewcontent/iat15.pdf
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spelling sg-smu-ink.sis_research-39282018-06-27T03:48:45Z Learning and Controlling Network Diffusion in Dependent Cascade Models DU, Jiali VARAKANTHAM, Pradeep Akshat KUMAR, CHENG, Shih-Fen Diffusion processes have increasingly been used to represent flow of ideas, traffic and diseases in networks. Learning and controlling the diffusion dynamics through management actions has been studied extensively in the context of independent cascade models, where diffusion on outgoing edges from a node are independent of each other. Our work, in contrast, addresses (a) learning diffusion taking management actions to alter the diffusion dynamics to achieve a desired outcome in dependent cascade models. A key characteristic of such dependent cascade models is the flow preservation at all nodes in the network. For example, traffic and people flow is preserved at each network node. As a case study, we address learning visitor mobility pattern at a theme park based on observed historical wait times at individual attractions, and use the learned model to plan management actions that reduce wait time at attractions. We test on real-world data from a theme park in Singapore and show that our learning approach can achieve an accuracy close to 80% for popular attractions, and the decision support algorithm can provide about 10-20% reduction in wait time. 2015-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2928 info:doi/10.1109/WI-IAT.2015.126 https://ink.library.smu.edu.sg/context/sis_research/article/3928/viewcontent/iat15.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial Intelligence and Robotics OS and Networks
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Artificial Intelligence and Robotics
OS and Networks
spellingShingle Artificial Intelligence and Robotics
OS and Networks
DU, Jiali
VARAKANTHAM, Pradeep
Akshat KUMAR,
CHENG, Shih-Fen
Learning and Controlling Network Diffusion in Dependent Cascade Models
description Diffusion processes have increasingly been used to represent flow of ideas, traffic and diseases in networks. Learning and controlling the diffusion dynamics through management actions has been studied extensively in the context of independent cascade models, where diffusion on outgoing edges from a node are independent of each other. Our work, in contrast, addresses (a) learning diffusion taking management actions to alter the diffusion dynamics to achieve a desired outcome in dependent cascade models. A key characteristic of such dependent cascade models is the flow preservation at all nodes in the network. For example, traffic and people flow is preserved at each network node. As a case study, we address learning visitor mobility pattern at a theme park based on observed historical wait times at individual attractions, and use the learned model to plan management actions that reduce wait time at attractions. We test on real-world data from a theme park in Singapore and show that our learning approach can achieve an accuracy close to 80% for popular attractions, and the decision support algorithm can provide about 10-20% reduction in wait time.
format text
author DU, Jiali
VARAKANTHAM, Pradeep
Akshat KUMAR,
CHENG, Shih-Fen
author_facet DU, Jiali
VARAKANTHAM, Pradeep
Akshat KUMAR,
CHENG, Shih-Fen
author_sort DU, Jiali
title Learning and Controlling Network Diffusion in Dependent Cascade Models
title_short Learning and Controlling Network Diffusion in Dependent Cascade Models
title_full Learning and Controlling Network Diffusion in Dependent Cascade Models
title_fullStr Learning and Controlling Network Diffusion in Dependent Cascade Models
title_full_unstemmed Learning and Controlling Network Diffusion in Dependent Cascade Models
title_sort learning and controlling network diffusion in dependent cascade models
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/sis_research/2928
https://ink.library.smu.edu.sg/context/sis_research/article/3928/viewcontent/iat15.pdf
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