Network Diffusions via Neural Mean-Field Dynamics
Network Diffusions via Neural Mean-Field Dynamics
复制标题
DOI：
--
复制
发表时间：
2020-06
期刊：
影响因子：
--
通讯作者：
Shushan He;H. Zha;X. Ye
中科院分区：
文献类型：
--
作者：
Shushan He;H. Zha;X. Ye
我们提出了一种基于神经平均场动力学的新颖学习框架，用于网络扩散的推理和估计问题。我们的新框架源自 Mori-Zwanzig 形式主义，以获得节点感染概率的精确演化，从而呈现延迟微分方程，其内存积分由可学习时间卷积算子近似，从而产生高度结构化和可解释的 RNN。直接使用级联数据，我们的框架可以共同学习扩散网络的结构和感染概率的演变，这是影响力最大化等重要下游应用的基石。参数学习和最优控制之间的联系也被建立。实证研究表明，我们的方法对底层扩散网络模型的变化具有通用性和鲁棒性，并且在合成数据和真实数据上的准确性和效率方面显着优于现有方法。
We propose a novel learning framework based on neural mean-field dynamics for inference and estimation problems of diffusion on networks. Our new framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities, which renders a delay differential equation with memory integral approximated by learnable time convolution operators, resulting in a highly structured and interpretable RNN. Directly using cascade data, our framework can jointly learn the structure of the diffusion network and the evolution of infection probabilities, which are cornerstone to important downstream applications such as influence maximization. Connections between parameter learning and optimal control are also established. Empirical study shows that our approach is versatile and robust to variations of the underlying diffusion network models, and significantly outperform existing approaches in accuracy and efficiency on both synthetic and real-world data.