Conference Proceeding Article
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.
OS and Networks
Intelligent Systems and Decision Analytics
2015 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT-15)
City or Country
DU, JIALI; VARAKANTHAM, Pradeep; Akshat KUMAR; and CHENG, Shih-Fen.
Learning and Controlling Network Diffusion in Dependent Cascade Models. (2015). 2015 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT-15). Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2928