Discriminative Learning of Infection Models – Nir Rosenfeld – 3.3.16

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.

Biography

Nir Rosenfeld is a graduate student at the Hebrew University under the supervision of Prof. Amir Globerson. Nir’s research focuses on analyzing interesting social problems from a machine learning perspective, and applying novel solutions to the derived complex learning problems. He has done work on adding socially-anchored high-order features for the task of link prediction, optimizing AUC in structured prediction models, constructing a learning framework for prediction in tasks regarding social dynamics, and has ongoing work on optimal tagging in social information systems. Nir also closely collaborates with psychologists and marketing researchers on studying the inherent links between faces and given names.