Modeling Information Propagation with Survival Theory

Manuel Gomez-Rodriguez, Jure Leskovec, Bernhard Schölkopf
; Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):666-674, 2013.

Abstract

Networks provide a ‘skeleton’ for the spread of contagions, like, information, ideas, behaviors and diseases. Many times networks over which contagions diffuse are unobserved and need to be inferred. Here we apply survival theory to develop general additive and multiplicative risk models under which the network inference problems can be solved efficiently by exploiting their convexity. Our additive risk model generalizes several existing network inference models. We show all these models are particular cases of our more general model. Our multiplicative model allows for modeling scenarios in which a node can either increase or decrease the risk of activation of another node, in contrast with previous approaches, which consider only positive risk increments. We evaluate the performance of our network inference algorithms on large synthetic and real cascade datasets, and show that our models are able to predict the length and duration of cascades in real data.

Cite this Paper


BibTeX
@InProceedings{pmlr-v28-gomez-rodriguez13, title = {Modeling Information Propagation with Survival Theory}, author = {Manuel Gomez-Rodriguez and Jure Leskovec and Bernhard Schölkopf}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {666--674}, year = {2013}, editor = {Sanjoy Dasgupta and David McAllester}, volume = {28}, number = {3}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/gomez-rodriguez13.pdf}, url = {http://proceedings.mlr.press/v28/gomez-rodriguez13.html}, abstract = {Networks provide a ‘skeleton’ for the spread of contagions, like, information, ideas, behaviors and diseases. Many times networks over which contagions diffuse are unobserved and need to be inferred. Here we apply survival theory to develop general additive and multiplicative risk models under which the network inference problems can be solved efficiently by exploiting their convexity. Our additive risk model generalizes several existing network inference models. We show all these models are particular cases of our more general model. Our multiplicative model allows for modeling scenarios in which a node can either increase or decrease the risk of activation of another node, in contrast with previous approaches, which consider only positive risk increments. We evaluate the performance of our network inference algorithms on large synthetic and real cascade datasets, and show that our models are able to predict the length and duration of cascades in real data.} }
Endnote
%0 Conference Paper %T Modeling Information Propagation with Survival Theory %A Manuel Gomez-Rodriguez %A Jure Leskovec %A Bernhard Schölkopf %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-gomez-rodriguez13 %I PMLR %J Proceedings of Machine Learning Research %P 666--674 %U http://proceedings.mlr.press %V 28 %N 3 %W PMLR %X Networks provide a ‘skeleton’ for the spread of contagions, like, information, ideas, behaviors and diseases. Many times networks over which contagions diffuse are unobserved and need to be inferred. Here we apply survival theory to develop general additive and multiplicative risk models under which the network inference problems can be solved efficiently by exploiting their convexity. Our additive risk model generalizes several existing network inference models. We show all these models are particular cases of our more general model. Our multiplicative model allows for modeling scenarios in which a node can either increase or decrease the risk of activation of another node, in contrast with previous approaches, which consider only positive risk increments. We evaluate the performance of our network inference algorithms on large synthetic and real cascade datasets, and show that our models are able to predict the length and duration of cascades in real data.
RIS
TY - CPAPER TI - Modeling Information Propagation with Survival Theory AU - Manuel Gomez-Rodriguez AU - Jure Leskovec AU - Bernhard Schölkopf BT - Proceedings of the 30th International Conference on Machine Learning PY - 2013/02/13 DA - 2013/02/13 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-gomez-rodriguez13 PB - PMLR SP - 666 DP - PMLR EP - 674 L1 - http://proceedings.mlr.press/v28/gomez-rodriguez13.pdf UR - http://proceedings.mlr.press/v28/gomez-rodriguez13.html AB - Networks provide a ‘skeleton’ for the spread of contagions, like, information, ideas, behaviors and diseases. Many times networks over which contagions diffuse are unobserved and need to be inferred. Here we apply survival theory to develop general additive and multiplicative risk models under which the network inference problems can be solved efficiently by exploiting their convexity. Our additive risk model generalizes several existing network inference models. We show all these models are particular cases of our more general model. Our multiplicative model allows for modeling scenarios in which a node can either increase or decrease the risk of activation of another node, in contrast with previous approaches, which consider only positive risk increments. We evaluate the performance of our network inference algorithms on large synthetic and real cascade datasets, and show that our models are able to predict the length and duration of cascades in real data. ER -
APA
Gomez-Rodriguez, M., Leskovec, J. & Schölkopf, B.. (2013). Modeling Information Propagation with Survival Theory. Proceedings of the 30th International Conference on Machine Learning, in PMLR 28(3):666-674

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