Stochastic blockmodeling of relational event dynamics

Christopher DuBois, Carter Butts, Padhraic Smyth
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, PMLR 31:238-246, 2013.

Abstract

Several approaches have recently been proposed for modeling of continuous-time network data via dyadic event rates conditioned on the observed history of events and nodal or dyadic covariates. In many cases, however, interaction propensities – and even the underlying mechanisms of interaction – vary systematically across subgroups whose identities are unobserved. For static networks such heterogeneity has been treated via methods such as stochastic blockmodeling, which operate by assuming latent groups of individuals with similar tendencies in their group-wise interactions. Here we combine ideas from stochastic blockmodeling and continuous-time network models by positing a latent partition of the node set such that event dynamics within and between subsets evolve in potentially distinct ways. We illustrate the use of our model family by application to several forms of dyadic interaction data, including email communication and Twitter direct messages. Parameter estimates from the fitted models clearly reveal heterogeneity in the dynamics among groups of individuals. We also find that the fitted models have better predictive accuracy than both baseline models and relational event models that lack latent structure.

Cite this Paper


BibTeX
@InProceedings{pmlr-v31-dubois13a, title = {Stochastic blockmodeling of relational event dynamics}, author = {DuBois, Christopher and Butts, Carter and Smyth, Padhraic}, booktitle = {Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics}, pages = {238--246}, year = {2013}, editor = {Carvalho, Carlos M. and Ravikumar, Pradeep}, volume = {31}, series = {Proceedings of Machine Learning Research}, address = {Scottsdale, Arizona, USA}, month = {29 Apr--01 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v31/dubois13a.pdf}, url = {https://proceedings.mlr.press/v31/dubois13a.html}, abstract = {Several approaches have recently been proposed for modeling of continuous-time network data via dyadic event rates conditioned on the observed history of events and nodal or dyadic covariates. In many cases, however, interaction propensities – and even the underlying mechanisms of interaction – vary systematically across subgroups whose identities are unobserved. For static networks such heterogeneity has been treated via methods such as stochastic blockmodeling, which operate by assuming latent groups of individuals with similar tendencies in their group-wise interactions. Here we combine ideas from stochastic blockmodeling and continuous-time network models by positing a latent partition of the node set such that event dynamics within and between subsets evolve in potentially distinct ways. We illustrate the use of our model family by application to several forms of dyadic interaction data, including email communication and Twitter direct messages. Parameter estimates from the fitted models clearly reveal heterogeneity in the dynamics among groups of individuals. We also find that the fitted models have better predictive accuracy than both baseline models and relational event models that lack latent structure. } }
Endnote
%0 Conference Paper %T Stochastic blockmodeling of relational event dynamics %A Christopher DuBois %A Carter Butts %A Padhraic Smyth %B Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2013 %E Carlos M. Carvalho %E Pradeep Ravikumar %F pmlr-v31-dubois13a %I PMLR %P 238--246 %U https://proceedings.mlr.press/v31/dubois13a.html %V 31 %X Several approaches have recently been proposed for modeling of continuous-time network data via dyadic event rates conditioned on the observed history of events and nodal or dyadic covariates. In many cases, however, interaction propensities – and even the underlying mechanisms of interaction – vary systematically across subgroups whose identities are unobserved. For static networks such heterogeneity has been treated via methods such as stochastic blockmodeling, which operate by assuming latent groups of individuals with similar tendencies in their group-wise interactions. Here we combine ideas from stochastic blockmodeling and continuous-time network models by positing a latent partition of the node set such that event dynamics within and between subsets evolve in potentially distinct ways. We illustrate the use of our model family by application to several forms of dyadic interaction data, including email communication and Twitter direct messages. Parameter estimates from the fitted models clearly reveal heterogeneity in the dynamics among groups of individuals. We also find that the fitted models have better predictive accuracy than both baseline models and relational event models that lack latent structure.
RIS
TY - CPAPER TI - Stochastic blockmodeling of relational event dynamics AU - Christopher DuBois AU - Carter Butts AU - Padhraic Smyth BT - Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics DA - 2013/04/29 ED - Carlos M. Carvalho ED - Pradeep Ravikumar ID - pmlr-v31-dubois13a PB - PMLR DP - Proceedings of Machine Learning Research VL - 31 SP - 238 EP - 246 L1 - http://proceedings.mlr.press/v31/dubois13a.pdf UR - https://proceedings.mlr.press/v31/dubois13a.html AB - Several approaches have recently been proposed for modeling of continuous-time network data via dyadic event rates conditioned on the observed history of events and nodal or dyadic covariates. In many cases, however, interaction propensities – and even the underlying mechanisms of interaction – vary systematically across subgroups whose identities are unobserved. For static networks such heterogeneity has been treated via methods such as stochastic blockmodeling, which operate by assuming latent groups of individuals with similar tendencies in their group-wise interactions. Here we combine ideas from stochastic blockmodeling and continuous-time network models by positing a latent partition of the node set such that event dynamics within and between subsets evolve in potentially distinct ways. We illustrate the use of our model family by application to several forms of dyadic interaction data, including email communication and Twitter direct messages. Parameter estimates from the fitted models clearly reveal heterogeneity in the dynamics among groups of individuals. We also find that the fitted models have better predictive accuracy than both baseline models and relational event models that lack latent structure. ER -
APA
DuBois, C., Butts, C. & Smyth, P.. (2013). Stochastic blockmodeling of relational event dynamics. Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 31:238-246 Available from https://proceedings.mlr.press/v31/dubois13a.html.

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