Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2013

Abstract

Diffusion processes in networks are increasingly used to model dynamic phenomena such as the spread of information, wildlife, or social influence. Our work addresses the problem of learning the underlying parameters that govern such a diffusion process by observing the time at which nodes become active. A key advantage of our approach is that, unlike previous work, it can tolerate missing observations for some nodes in the diffusion process. Having incomplete observations is characteristic of offline networks used to model the spread of wildlife. We develop an EM algorithm to address parameter learning in such settings. Since both the E and M steps are computationally challenging, we employ a number of optimization methods such as nonlinear and difference-of-convex programming to address these challenges. Evaluation of the approach on the Red-cockaded Woodpecker conservation problem shows that it is highly robust and accurately learns parameters in various settings, even with more than 80% missing data.

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 3-9

First Page

2923

Last Page

2930

ISBN

9781577356332

Publisher

AAAI Press

City or Country

Menlo Park, CA

Copyright Owner and License

Authors

Additional URL

https://www.ijcai.org/Proceedings/13/Papers/429.pdf

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