Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

7-2013

Abstract

Diffusion processes in networks are increasingly used to model the spread of information and social influence. In several applications in computational sustainability such as the spread of wildlife, infectious diseases and traffic mobility pattern, the observed data often consists of only aggregate information. In this work, we present new models that generalize standard diffusion processes to such collective settings. We also present optimization based techniques that can accurately learn the underlying dynamics of the given contagion process, including the hidden network structure, by only observing the time a node becomes active and the associated aggregate information. Empirically, our technique is highly robust and accurately learns network structure with more than 90% recall and precision. Results on real-world flu spread data in the US confirm that our technique can also accurately model infectious disease spread.

Discipline

Artificial Intelligence and Robotics | Computer Sciences

Research Areas

Intelligent Systems and Decision Analytics

Publication

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)

First Page

351

Last Page

360

ISBN

9780974903996

Publisher

AUAI Press

City or Country

Corvallis, OR

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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