Discriminative learning of infection models

Nir Rosenfeld, Mor Nitzan, Amir Globerson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Infection and diffusion processes over networks arise in many domains. These introduce many challenging prediction tasks, such as influence estimation, trend prediction, and epidemic source localization. The standard approach to such problems is generative: assume an underlying infection model, learn its parameters, and infer the required output. In order to learn efficiently, the chosen infection models are often simple, and learning is focused on inferring the parameters of the model rather than on optimizing prediction accuracy. Here we argue that for prediction tasks, a discriminative approach is more adequate. We introduce DIMPLE, a novel discriminative learning framework for training classifiers based on dynamic infection models. We show how highly non-linear predictors based on infection models can be "linearized" by considering a larger class of prediction functions. Efficient learning over this class is performed by constructing "infection kernels" based on the outputs of infection models, and can be plugged into any kernelsupporting framework. DIMPLE can be applied to virtually any infection-related prediction task and any infection model for which the desired output can be calculated or simulated. For influence estimation in well-known infection models, we show that the kernel can either be computed in closed form, or reduces to estimating co-influence of seed pairs. We apply DIMPLE to the tasks of influence estimation on synthetic and real data from Digg, and to predicting customer network value in Polly, a viral phone-based development-related service deployed in low-literate communities. Our results show that DIMPLE outperforms strong baselines.

Original languageEnglish
Title of host publicationWSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages563-572
Number of pages10
ISBN (Electronic)9781450337168
DOIs
StatePublished - 8 Feb 2016
Event9th ACM International Conference on Web Search and Data Mining, WSDM 2016 - San Francisco, United States
Duration: 22 Feb 201625 Feb 2016

Publication series

NameWSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining

Conference

Conference9th ACM International Conference on Web Search and Data Mining, WSDM 2016
Country/TerritoryUnited States
CitySan Francisco
Period22/02/1625/02/16

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