Discriminative Learning of Infection Models

Nir Rosenfeld, M. Nitzan, A. Globerson
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引用次数: 16

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 kernel-supporting 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.
感染模型的判别学习
网络上的感染和扩散过程出现在许多领域。这些引入了许多具有挑战性的预测任务,如影响估计、趋势预测和流行病源定位。解决这类问题的标准方法是生成式的:假设一个潜在的感染模型,学习其参数,并推断所需的输出。为了有效地学习,所选择的感染模型往往是简单的,学习的重点是推断模型的参数,而不是优化预测精度。在这里,我们认为对于预测任务,判别方法更合适。我们介绍了一种基于动态感染模型训练分类器的新型判别学习框架DIMPLE。我们展示了基于感染模型的高度非线性预测因子如何通过考虑更大的预测函数类来“线性化”。通过构建基于感染模型输出的“感染核”,对该类进行有效的学习,并且可以插入任何支持核的框架中。DIMPLE可以应用于几乎任何与感染相关的预测任务和任何可以计算或模拟所需输出的感染模型。对于已知侵染模型的影响估计,我们证明核可以以封闭形式计算,也可以简化为估计种子对的共影响。我们将DIMPLE应用于对Digg合成数据和真实数据的影响评估任务,并预测Polly的客户网络价值,Polly是一种部署在低文化社区的基于病毒式电话的发展相关服务。我们的结果表明,DIMPLE优于强基线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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