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 increas- ingly used to model the spread of informa- tion and social influence. In several applica- tions in computational sustainability such as the spread of wildlife, infectious diseases and traffic mobility pattern, the observed data of- ten consists of only aggregate information. In this work, we present new models that gener- alize standard diffusion processes to such col- lective settings. We also present optimization based techniques that can accurately learn the underlying dynamics of the given conta- gion 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 pre- cision. 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 increas- ingly used to model the spread of informa- tion and social influence. In several applica- tions in computational sustainability such as the spread of wildlife, infectious diseases and traffic mobility pattern, the observed data of- ten consists of only aggregate information. In this work, we present new models that gener- alize standard diffusion processes to such col- lective settings. We also present optimization based techniques that can accurately learn the underlying dynamics of the given conta- gion 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 pre- cision. 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 increas- ingly used to model the spread of informa- tion and social influence. In several applica- tions in computational sustainability such as the spread of wildlife, infectious diseases and traffic mobility pattern, the observed data of- ten consists of only aggregate information. In this work, we present new models that gener- alize standard diffusion processes to such col- lective settings. We also present optimization based techniques that can accurately learn the underlying dynamics of the given conta- gion 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 pre- cision. 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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