Collective Diffusion Over Networks: Models and Inference

Akshat Kumar, Dan Sheldon, Biplav Srivastava
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:481-490, 2013.

Abstract

Diffusion processes in networks are increasingly used to model the spread of information and social influence. In several applications in computational sustainability such as the spread of wildlife, infectious diseases and traffic mobility pattern, the observed data often consists of only aggregate information. In this work, we present new models that generalize standard diffusion processes to such collective settings. We also present optimization based techniques that can accurately learn the underlying dynamics of the given contagion process, including the hidden network structure, by only observing the time a node becomes active and the associated aggregate information. Empirically, our technique is highly robust and accurately learns network structure with more than 90% recall and precision. Results on real-world flu spread data in the US confirm that our technique can also accurately model infectious disease spread.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR11-kumar13a, title = {Collective Diffusion Over Networks: Models and Inference}, author = {Kumar, Akshat and Sheldon, Dan and Srivastava, Biplav}, booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence}, pages = {481--490}, year = {2013}, editor = {Nicholson, Ann and Smyth, Padhraic}, volume = {R11}, series = {Proceedings of Machine Learning Research}, month = {12--14 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r11/main/assets/kumar13a/kumar13a.pdf}, url = {https://proceedings.mlr.press/r11/kumar13a.html}, abstract = {Diffusion processes in networks are increasingly used to model the spread of information and social influence. In several applications in computational sustainability such as the spread of wildlife, infectious diseases and traffic mobility pattern, the observed data often consists of only aggregate information. In this work, we present new models that generalize standard diffusion processes to such collective settings. We also present optimization based techniques that can accurately learn the underlying dynamics of the given contagion process, including the hidden network structure, by only observing the time a node becomes active and the associated aggregate information. Empirically, our technique is highly robust and accurately learns network structure with more than 90% recall and precision. Results on real-world flu spread data in the US confirm that our technique can also accurately model infectious disease spread.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Collective Diffusion Over Networks: Models and Inference %A Akshat Kumar %A Dan Sheldon %A Biplav Srivastava %B Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2013 %E Ann Nicholson %E Padhraic Smyth %F pmlr-vR11-kumar13a %I PMLR %P 481--490 %U https://proceedings.mlr.press/r11/kumar13a.html %V R11 %X Diffusion processes in networks are increasingly used to model the spread of information and social influence. In several applications in computational sustainability such as the spread of wildlife, infectious diseases and traffic mobility pattern, the observed data often consists of only aggregate information. In this work, we present new models that generalize standard diffusion processes to such collective settings. We also present optimization based techniques that can accurately learn the underlying dynamics of the given contagion process, including the hidden network structure, by only observing the time a node becomes active and the associated aggregate information. Empirically, our technique is highly robust and accurately learns network structure with more than 90% recall and precision. Results on real-world flu spread data in the US confirm that our technique can also accurately model infectious disease spread. %Z Reissued by PMLR on 04 October 2026.
APA
Kumar, A., Sheldon, D. & Srivastava, B.. (2013). Collective Diffusion Over Networks: Models and Inference. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R11:481-490 Available from https://proceedings.mlr.press/r11/kumar13a.html. Reissued by PMLR on 04 October 2026.

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