Estimating Diffusion Probability Changes for AsIC-SIS Model from Information Diffusion Results

Akihiro Koide, Kazumi Saito, Kouzou Ohara, Masahiro Kimura, Hiroshi Motoda
Proceedings of the Asian Conference on Machine Learning, PMLR 20:297-313, 2011.

Abstract

We address the problem of estimating changes in diffusion probability over a social network from the observed information diffusion results, which is possibly caused by an unknown external situation change. For this problem, we focused on the asynchronous independent cascade (AsIC) model in the SIS (Susceptible/Infected/Susceptible) setting in order to meet more realistic situations such as communication in a blogosphere. This model is referred to as the AsIC-SIS model. We assume that the diffusion parameter changes are approximated by a series of step functions, and their changes are reflected in the observed diffusion results. Thus, the problem is reduced to detecting how many step functions are needed, where in time each one starts and how long it lasts, and what the hight of each one is. The method employs the derivative of the likelihood function of the observed data that are assumed to be generated from the AsIC-SIS model, adopts a divide-and-conquer type greedy recursive partitioning, and utilizes an MDL model selection measure to determine the adequate number of step functions. The results obtained using real world network structures confirmed that the method works well as intended. The MDL criterion is useful to avoid overfitting, and the found pattern is not necessarily the same in terms of the number of step functions as the one assumed to be true, but the error is always reduced to a small value.

Cite this Paper

BibTeX
@InProceedings{pmlr-v20-koide11, title = {Estimating Diffusion Probability Changes for {AsIC-SIS} Model from Information Diffusion Results}, author = {Koide, Akihiro and Saito, Kazumi and Ohara, Kouzou and Kimura, Masahiro and Motoda, Hiroshi}, booktitle = {Proceedings of the Asian Conference on Machine Learning}, pages = {297--313}, year = {2011}, editor = {Hsu, Chun-Nan and Lee, Wee Sun}, volume = {20}, series = {Proceedings of Machine Learning Research}, address = {South Garden Hotels and Resorts, Taoyuan, Taiwain}, month = {14--15 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v20/koide11/koide11.pdf}, url = {https://proceedings.mlr.press/v20/koide11.html}, abstract = {We address the problem of estimating changes in diffusion probability over a social network from the observed information diffusion results, which is possibly caused by an unknown external situation change. For this problem, we focused on the asynchronous independent cascade (AsIC) model in the SIS (Susceptible/Infected/Susceptible) setting in order to meet more realistic situations such as communication in a blogosphere. This model is referred to as the AsIC-SIS model. We assume that the diffusion parameter changes are approximated by a series of step functions, and their changes are reflected in the observed diffusion results. Thus, the problem is reduced to detecting how many step functions are needed, where in time each one starts and how long it lasts, and what the hight of each one is. The method employs the derivative of the likelihood function of the observed data that are assumed to be generated from the AsIC-SIS model, adopts a divide-and-conquer type greedy recursive partitioning, and utilizes an MDL model selection measure to determine the adequate number of step functions. The results obtained using real world network structures confirmed that the method works well as intended. The MDL criterion is useful to avoid overfitting, and the found pattern is not necessarily the same in terms of the number of step functions as the one assumed to be true, but the error is always reduced to a small value.} }
Endnote
%0 Conference Paper %T Estimating Diffusion Probability Changes for AsIC-SIS Model from Information Diffusion Results %A Akihiro Koide %A Kazumi Saito %A Kouzou Ohara %A Masahiro Kimura %A Hiroshi Motoda %B Proceedings of the Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2011 %E Chun-Nan Hsu %E Wee Sun Lee %F pmlr-v20-koide11 %I PMLR %P 297--313 %U https://proceedings.mlr.press/v20/koide11.html %V 20 %X We address the problem of estimating changes in diffusion probability over a social network from the observed information diffusion results, which is possibly caused by an unknown external situation change. For this problem, we focused on the asynchronous independent cascade (AsIC) model in the SIS (Susceptible/Infected/Susceptible) setting in order to meet more realistic situations such as communication in a blogosphere. This model is referred to as the AsIC-SIS model. We assume that the diffusion parameter changes are approximated by a series of step functions, and their changes are reflected in the observed diffusion results. Thus, the problem is reduced to detecting how many step functions are needed, where in time each one starts and how long it lasts, and what the hight of each one is. The method employs the derivative of the likelihood function of the observed data that are assumed to be generated from the AsIC-SIS model, adopts a divide-and-conquer type greedy recursive partitioning, and utilizes an MDL model selection measure to determine the adequate number of step functions. The results obtained using real world network structures confirmed that the method works well as intended. The MDL criterion is useful to avoid overfitting, and the found pattern is not necessarily the same in terms of the number of step functions as the one assumed to be true, but the error is always reduced to a small value.
RIS
TY - CPAPER TI - Estimating Diffusion Probability Changes for AsIC-SIS Model from Information Diffusion Results AU - Akihiro Koide AU - Kazumi Saito AU - Kouzou Ohara AU - Masahiro Kimura AU - Hiroshi Motoda BT - Proceedings of the Asian Conference on Machine Learning DA - 2011/11/17 ED - Chun-Nan Hsu ED - Wee Sun Lee ID - pmlr-v20-koide11 PB - PMLR DP - Proceedings of Machine Learning Research VL - 20 SP - 297 EP - 313 L1 - http://proceedings.mlr.press/v20/koide11/koide11.pdf UR - https://proceedings.mlr.press/v20/koide11.html AB - We address the problem of estimating changes in diffusion probability over a social network from the observed information diffusion results, which is possibly caused by an unknown external situation change. For this problem, we focused on the asynchronous independent cascade (AsIC) model in the SIS (Susceptible/Infected/Susceptible) setting in order to meet more realistic situations such as communication in a blogosphere. This model is referred to as the AsIC-SIS model. We assume that the diffusion parameter changes are approximated by a series of step functions, and their changes are reflected in the observed diffusion results. Thus, the problem is reduced to detecting how many step functions are needed, where in time each one starts and how long it lasts, and what the hight of each one is. The method employs the derivative of the likelihood function of the observed data that are assumed to be generated from the AsIC-SIS model, adopts a divide-and-conquer type greedy recursive partitioning, and utilizes an MDL model selection measure to determine the adequate number of step functions. The results obtained using real world network structures confirmed that the method works well as intended. The MDL criterion is useful to avoid overfitting, and the found pattern is not necessarily the same in terms of the number of step functions as the one assumed to be true, but the error is always reduced to a small value. ER -
APA
Koide, A., Saito, K., Ohara, K., Kimura, M. & Motoda, H.. (2011). Estimating Diffusion Probability Changes for AsIC-SIS Model from Information Diffusion Results. Proceedings of the Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 20:297-313 Available from https://proceedings.mlr.press/v20/koide11.html.

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