Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2015

Abstract

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.

Discipline

Artificial Intelligence and Robotics | OS and Networks

Research Areas

Intelligent Systems and Optimization

Publication

2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology WI-IAT 2015: 6-9 December, Singapore

ISBN

9781467396189

Identifier

10.1109/WI-IAT.2015.126

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/WI-IAT.2015.126

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