用于建模动态网络的互激发点过程图
Mutually exciting point process graphs for modelling dynamic networks
AI总结：
提出互激发点过程图（MEG）这一动态网络模型，结合互激发点过程与潜在空间模型估计事件依赖与节点关系，支持未观测边的强度估计，通过梯度上升和EM算法实现快速推断，在异常检测中表现优异。
摘要：
提出了一类新的动态网络模型，称为互激发点过程图（MEG）。MEG是一种可扩展的网络级统计模型，适用于具有二元标记的点过程，可在评估未来事件（包括节点间先前未观察到的连接）的显著性时用于异常检测。该模型结合了互激发点过程来估计事件间的依赖关系，以及潜在空间模型来推断节点间的关系。每条网络边的强度函数仅由节点特定参数表征，这使得信息能够在整个网络中共享。这种构造使得即使对于未观察到的边也能进行强度估计，这在现实世界应用中尤为重要，例如网络安全中的计算机网络。本文推导了MEG对数似然函数的递归形式，并利用现代梯度上升算法推导出快速推断程序。此外还推导了一种替代的EM算法。该模型和算法在模拟图和真实世界数据集上进行了测试，表现出优异的性能。
英文摘要：
A new class of models for dynamic networks is proposed, called mutually exciting point process graphs (MEG). MEG is a scalable network-wide statistical model for point processes with dyadic marks, which can be used for anomaly detection when assessing the significance of future events, including previously unobserved connections between nodes. The model combines mutually exciting point processes to estimate dependencies between events and latent space models to infer relationships between the nodes. The intensity functions for each network edge are characterised exclusively by node-specific parameters, which allows information to be shared across the network. This construction enables estimation of intensities even for unobserved edges, which is particularly important in real world applications, such as computer networks arising in cyber-security. A recursive form of the log-likelihood function for MEG is obtained, which is used to derive fast inferential procedures via modern gradient ascent algorithms. An alternative EM algorithm is also derived. The model and algorithms are tested on simulated graphs and real world datasets, demonstrating excellent performance.
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。
