Learning Attribute-weighted Voter Model over Social Networks

Yuki Yamagishi, Kazumi Saito, Kouzou Ohara, Masahiro Kimura, Hiroshi Motoda
Proceedings of the Asian Conference on Machine Learning, PMLR 20:263-280, 2011.

Abstract

We propose an opinion formation model, an extension of the voter model that incorporates the strength of each node, which is modeled as a function of the node attributes. Then, we address the problem of estimating parameter values for these attributes that appear in the function from the observed opinion formation data and solve this by maximizing the likelihood using an iterative parameter value updating algorithm, which is efficient and is guaranteed to converge. We show that the proposed algorithm can correctly learn the dependency in our experiments on four real world networks for which we used the assumed attribute dependency. We further show that the influence degree of each node based on the extended voter model is substantially different from that obtained assuming a uniform strength (a naive model for which the influence degree is known to be proportional to the node degree), and is more sensitive to the node strength than the node degree even for a moderate value of the node strength.

Cite this Paper

BibTeX
@InProceedings{pmlr-v20-yamagishi11, title = {Learning Attribute-weighted Voter Model over Social Networks}, author = {Yamagishi, Yuki and Saito, Kazumi and Ohara, Kouzou and Kimura, Masahiro and Motoda, Hiroshi}, booktitle = {Proceedings of the Asian Conference on Machine Learning}, pages = {263--280}, 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/yamagishi11/yamagishi11.pdf}, url = {https://proceedings.mlr.press/v20/yamagishi11.html}, abstract = {We propose an opinion formation model, an extension of the voter model that incorporates the strength of each node, which is modeled as a function of the node attributes. Then, we address the problem of estimating parameter values for these attributes that appear in the function from the observed opinion formation data and solve this by maximizing the likelihood using an iterative parameter value updating algorithm, which is efficient and is guaranteed to converge. We show that the proposed algorithm can correctly learn the dependency in our experiments on four real world networks for which we used the assumed attribute dependency. We further show that the influence degree of each node based on the extended voter model is substantially different from that obtained assuming a uniform strength (a naive model for which the influence degree is known to be proportional to the node degree), and is more sensitive to the node strength than the node degree even for a moderate value of the node strength.} }
Endnote
%0 Conference Paper %T Learning Attribute-weighted Voter Model over Social Networks %A Yuki Yamagishi %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-yamagishi11 %I PMLR %P 263--280 %U https://proceedings.mlr.press/v20/yamagishi11.html %V 20 %X We propose an opinion formation model, an extension of the voter model that incorporates the strength of each node, which is modeled as a function of the node attributes. Then, we address the problem of estimating parameter values for these attributes that appear in the function from the observed opinion formation data and solve this by maximizing the likelihood using an iterative parameter value updating algorithm, which is efficient and is guaranteed to converge. We show that the proposed algorithm can correctly learn the dependency in our experiments on four real world networks for which we used the assumed attribute dependency. We further show that the influence degree of each node based on the extended voter model is substantially different from that obtained assuming a uniform strength (a naive model for which the influence degree is known to be proportional to the node degree), and is more sensitive to the node strength than the node degree even for a moderate value of the node strength.
RIS
TY - CPAPER TI - Learning Attribute-weighted Voter Model over Social Networks AU - Yuki Yamagishi 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-yamagishi11 PB - PMLR DP - Proceedings of Machine Learning Research VL - 20 SP - 263 EP - 280 L1 - http://proceedings.mlr.press/v20/yamagishi11/yamagishi11.pdf UR - https://proceedings.mlr.press/v20/yamagishi11.html AB - We propose an opinion formation model, an extension of the voter model that incorporates the strength of each node, which is modeled as a function of the node attributes. Then, we address the problem of estimating parameter values for these attributes that appear in the function from the observed opinion formation data and solve this by maximizing the likelihood using an iterative parameter value updating algorithm, which is efficient and is guaranteed to converge. We show that the proposed algorithm can correctly learn the dependency in our experiments on four real world networks for which we used the assumed attribute dependency. We further show that the influence degree of each node based on the extended voter model is substantially different from that obtained assuming a uniform strength (a naive model for which the influence degree is known to be proportional to the node degree), and is more sensitive to the node strength than the node degree even for a moderate value of the node strength. ER -
APA
Yamagishi, Y., Saito, K., Ohara, K., Kimura, M. & Motoda, H.. (2011). Learning Attribute-weighted Voter Model over Social Networks. Proceedings of the Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 20:263-280 Available from https://proceedings.mlr.press/v20/yamagishi11.html.

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