标题： 在点过程数据中发现潜在的网络结构 显示英文标题

标题： Discovering Latent Network Structure in Point Process Data

摘要： 网络在现代数据分析中起着核心作用，使我们能够通过研究系统各部分之间的关系来推断系统。 在大多数网络分析中，边通常是已知的。 然而，在许多系统中，直接测量网络是困难甚至不可能的。 潜在网络的例子包括连接金融工具的经济互动和帮派暴力中的互惠模式。 在这种情况下，我们只能获得与每个节点相关的事件的噪声观测值。 为了能够分析这些隐含网络，我们开发了一种概率模型，该模型结合了相互激励点过程与随机图模型。 我们展示了泊松叠加原理如何能促成一个优雅的辅助变量公式化以及一种完全贝叶斯、并行推理算法。 我们在几个数据集上对该新模型进行了实证评估。 显示英文摘要

摘要： Networks play a central role in modern data analysis, enabling us to reason about systems by studying the relationships between their parts. Most often in network analysis, the edges are given. However, in many systems it is difficult or impossible to measure the network directly. Examples of latent networks include economic interactions linking financial instruments and patterns of reciprocity in gang violence. In these cases, we are limited to noisy observations of events associated with each node. To enable analysis of these implicit networks, we develop a probabilistic model that combines mutually-exciting point processes with random graph models. We show how the Poisson superposition principle enables an elegant auxiliary variable formulation and a fully-Bayesian, parallel inference algorithm. We evaluate this new model empirically on several datasets.