Efficient Estimation of Influence Functions for SIS Model on Social Networks
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 2046-2050.
Abstract
We address the problem of efficiently estimating the influence function of initially activated nodes in a social network under the susceptible / infected / susceptible (SIS) model, a diffusion model where nodes are allowed to be activated multiple times. The computational complexity drastically increases because of this multiple activation property. We solve this problem by constructing a layered graph from the original social network with each layer added on top as the time proceeds, and applying the bond percolation with a pruning strategy. We show that the computational complexity of the proposed method is much smaller than the conventional naive probabilistic simulation method by a theoretical analysis and confirm this by applying the proposed method to two real world networks.Masahiro Kimura, Kazumi Saito, Hiroshi Motoda
@inproceedings{ijcai2009p337,
title = {Efficient Estimation of Influence Functions for SIS Model on Social Networks},
author = {Kimura, Masahiro and Saito, Kazumi and Motoda, Hiroshi},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {2046--2050},
year = {2009},
url = {https://ijcai.org/proceedings/2009/337},
}
TY - CPAPER TI - Efficient Estimation of Influence Functions for SIS Model on Social Networks AU - Kimura, Masahiro AU - Saito, Kazumi AU - Motoda, Hiroshi T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 2046 EP - 2050 UR - https://ijcai.org/proceedings/2009/337 ER -Copied
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