A Dynamic Relational Infinite Feature Model for Longitudinal Social Networks

James Foulds, Christopher DuBois, Arthur Asuncion, Carter Butts, Padhraic Smyth
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:287-295, 2011.

Abstract

Real-world relational data sets, such as social networks, often involve measurements over time. We propose a Bayesian nonparametric latent feature model for such data, where the latent features for each actor in the network evolve according to a Markov process, extending recent work on similar models for static networks. We show how the number of features and their trajectories for each actor can be inferred simultaneously and demonstrate the utility of this model on prediction tasks using both synthetic and real-world data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v15-foulds11b, title = {A Dynamic Relational Infinite Feature Model for Longitudinal Social Networks}, author = {Foulds, James and DuBois, Christopher and Asuncion, Arthur and Butts, Carter and Smyth, Padhraic}, booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics}, pages = {287--295}, year = {2011}, editor = {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav}, volume = {15}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v15/foulds11b/foulds11b.pdf}, url = {https://proceedings.mlr.press/v15/foulds11b.html}, abstract = {Real-world relational data sets, such as social networks, often involve measurements over time. We propose a Bayesian nonparametric latent feature model for such data, where the latent features for each actor in the network evolve according to a Markov process, extending recent work on similar models for static networks. We show how the number of features and their trajectories for each actor can be inferred simultaneously and demonstrate the utility of this model on prediction tasks using both synthetic and real-world data.} }
Endnote
%0 Conference Paper %T A Dynamic Relational Infinite Feature Model for Longitudinal Social Networks %A James Foulds %A Christopher DuBois %A Arthur Asuncion %A Carter Butts %A Padhraic Smyth %B Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2011 %E Geoffrey Gordon %E David Dunson %E Miroslav Dudík %F pmlr-v15-foulds11b %I PMLR %P 287--295 %U https://proceedings.mlr.press/v15/foulds11b.html %V 15 %X Real-world relational data sets, such as social networks, often involve measurements over time. We propose a Bayesian nonparametric latent feature model for such data, where the latent features for each actor in the network evolve according to a Markov process, extending recent work on similar models for static networks. We show how the number of features and their trajectories for each actor can be inferred simultaneously and demonstrate the utility of this model on prediction tasks using both synthetic and real-world data.
RIS
TY - CPAPER TI - A Dynamic Relational Infinite Feature Model for Longitudinal Social Networks AU - James Foulds AU - Christopher DuBois AU - Arthur Asuncion AU - Carter Butts AU - Padhraic Smyth BT - Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics DA - 2011/06/14 ED - Geoffrey Gordon ED - David Dunson ED - Miroslav Dudík ID - pmlr-v15-foulds11b PB - PMLR DP - Proceedings of Machine Learning Research VL - 15 SP - 287 EP - 295 L1 - http://proceedings.mlr.press/v15/foulds11b/foulds11b.pdf UR - https://proceedings.mlr.press/v15/foulds11b.html AB - Real-world relational data sets, such as social networks, often involve measurements over time. We propose a Bayesian nonparametric latent feature model for such data, where the latent features for each actor in the network evolve according to a Markov process, extending recent work on similar models for static networks. We show how the number of features and their trajectories for each actor can be inferred simultaneously and demonstrate the utility of this model on prediction tasks using both synthetic and real-world data. ER -
APA
Foulds, J., DuBois, C., Asuncion, A., Butts, C. & Smyth, P.. (2011). A Dynamic Relational Infinite Feature Model for Longitudinal Social Networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 15:287-295 Available from https://proceedings.mlr.press/v15/foulds11b.html.

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