Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks

Creighton Heaukulani, Zoubin Ghahramani
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(1):275-283, 2013.

Abstract

Current Bayesian models for dynamic social network data have focused on modelling the influence of evolving unobserved structure on observed social interactions. However, an understanding of how observed social relationships from the past affect future unobserved structure in the network has been neglected. In this paper, we introduce a new probabilistic model for capturing this phenomenon, which we call latent feature propagation, in social networks. We demonstrate our model’s capability for inferring such latent structure in varying types of social network datasets, and experimental studies show this structure achieves higher predictive performance on link prediction and forecasting tasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v28-heaukulani13, title = {Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks}, author = {Heaukulani, Creighton and Ghahramani, Zoubin}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {275--283}, year = {2013}, editor = {Dasgupta, Sanjoy and McAllester, David}, volume = {28}, number = {1}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/heaukulani13.pdf}, url = {https://proceedings.mlr.press/v28/heaukulani13.html}, abstract = {Current Bayesian models for dynamic social network data have focused on modelling the influence of evolving unobserved structure on observed social interactions. However, an understanding of how observed social relationships from the past affect future unobserved structure in the network has been neglected. In this paper, we introduce a new probabilistic model for capturing this phenomenon, which we call latent feature propagation, in social networks. We demonstrate our model’s capability for inferring such latent structure in varying types of social network datasets, and experimental studies show this structure achieves higher predictive performance on link prediction and forecasting tasks.} }
Endnote
%0 Conference Paper %T Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks %A Creighton Heaukulani %A Zoubin Ghahramani %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-heaukulani13 %I PMLR %P 275--283 %U https://proceedings.mlr.press/v28/heaukulani13.html %V 28 %N 1 %X Current Bayesian models for dynamic social network data have focused on modelling the influence of evolving unobserved structure on observed social interactions. However, an understanding of how observed social relationships from the past affect future unobserved structure in the network has been neglected. In this paper, we introduce a new probabilistic model for capturing this phenomenon, which we call latent feature propagation, in social networks. We demonstrate our model’s capability for inferring such latent structure in varying types of social network datasets, and experimental studies show this structure achieves higher predictive performance on link prediction and forecasting tasks.
RIS
TY - CPAPER TI - Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks AU - Creighton Heaukulani AU - Zoubin Ghahramani BT - Proceedings of the 30th International Conference on Machine Learning DA - 2013/02/13 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-heaukulani13 PB - PMLR DP - Proceedings of Machine Learning Research VL - 28 IS - 1 SP - 275 EP - 283 L1 - http://proceedings.mlr.press/v28/heaukulani13.pdf UR - https://proceedings.mlr.press/v28/heaukulani13.html AB - Current Bayesian models for dynamic social network data have focused on modelling the influence of evolving unobserved structure on observed social interactions. However, an understanding of how observed social relationships from the past affect future unobserved structure in the network has been neglected. In this paper, we introduce a new probabilistic model for capturing this phenomenon, which we call latent feature propagation, in social networks. We demonstrate our model’s capability for inferring such latent structure in varying types of social network datasets, and experimental studies show this structure achieves higher predictive performance on link prediction and forecasting tasks. ER -
APA
Heaukulani, C. & Ghahramani, Z.. (2013). Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks. Proceedings of the 30th International Conference on Machine Learning, in Proceedings of Machine Learning Research 28(1):275-283 Available from https://proceedings.mlr.press/v28/heaukulani13.html.

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