基于部分观测的独立级联动力学预测中心学习
Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations
摘要：
传播过程在传播网络、信息传播、市场营销和意见设定等建模中发挥着越来越重要的作用。我们解决的扩散模型的学习问题，使得从该模型生成的预测是准确的，并可以随后用于扩散动力学的优化和控制。我们专注于一个具有挑战性的设置，其中动态的完整观察是不可用的，标准的方法，如最大似然很快成为棘手的大型网络实例。我们引入了一种基于可扩展动态消息传递方法的计算高效算法，该算法能够在仅给定网络中节点激活时间的有限信息的情况下学习有效传播模型的参数。流行的独立级联模型被用来说明我们的方法。我们表明，从学习模型中进行的易处理的推断与原始模型相比，可以更好地预测边际概率。我们开发了一个系统的学习混合模型的过程，进一步提高了预测质量。
英文摘要：
Spreading processes play an increasingly important role in modeling for diffusion networks, information propagation, marketing and opinion setting. We address the problem of learning of a spreading model such that the predictions generated from this model are accurate and could be subsequently used for the optimization, and control of diffusion dynamics. We focus on a challenging setting where full observations of the dynamics are not available, and standard approaches such as maximum likelihood quickly become intractable for large network instances. We introduce a computationally efficient algorithm, based on a scalable dynamic message-passing approach, which is able to learn parameters of the effective spreading model given only limited information on the activation times of nodes in the network. The popular Independent Cascade model is used to illustrate our approach. We show that tractable inference from the learned model generates a better prediction of marginal probabilities compared to the original model. We develop a systematic procedure for learning a mixture of models which further improves the prediction quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
