图 书 馆
网络上动态点过程的跟踪Tracking dynamic point processes on networks |
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| 课程网址: | http://videolectures.net/sahd2014_willett_tracking_dynamic/ |
| 主讲教师: | Rebecca Willett |
| 开课单位: | 威斯康星大学 |
| 开课时间: | 2014-10-29 |
| 课程语种: | 英语 |
| 中文简介: |  级联事件是许多现实世界中社会，生物和金融网络的显着特征。在社交网络中，社交互惠是帮派互动的报复，民族国家冲突中的代理战争或通过社交媒体共享的互联网模因的报复。神经元尖峰刺激或抑制其他神经元的尖峰活动。股市冲击可能会触发整个金融网络的波及。在这些和其他示例中，通常只观察到与网络节点关联的单个事件，而通常不知道节点之间的底层动态关系。本文解决了跟踪此类网络中的事件如何刺激或影响未来事件的挑战。所提出的方法是一个非常适合于流数据的在线学习框架，它使用多变量Hawkes点过程模型来封装社交网络中观察到的事件的自回归特征。动态环境中在线学习的最新工作不仅可以利用底层网络中的动态，还可以跟踪网络的发展。后悔的界限和实验结果表明，该方法的性能几乎与预言或批处理算法一样好。 |
| 课程简介: | Cascading chains of events are a salient feature of many real-world social, biological, and financial networks. In social networks, social reciprocity accounts for retaliations in gang interactions, proxy wars in nation-state conflicts, or Internet memes shared via social media. Neuron spikes stimulate or inhibit spike activity in other neurons. Stock market shocks can trigger a contagion of volatility throughout a financial network. In these and other examples, only individual events associated with network nodes are observed, usually without knowledge of the underlying dynamic relationships between nodes. This paper addresses the challenge of tracking how events within such networks stimulate or influence future events. The proposed approach is an online learning framework well-suited to streaming data, using a multivariate Hawkes point process model to encapsulate autoregressive features of observed events within the social network. Recent work on online learning in dynamic environments is leveraged not only to exploit the dynamics within the underlying network, but also to track that network structure as it evolves. Regret bounds and experimental results demonstrate that the proposed method performs nearly as well as an oracle or batch algorithm. |
| 关 键 词: | 模型分析; 动态环境 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-10-12：zyk |
| 最后编审: | 2020-10-12：zyk |
| 阅读次数: | 50 |