CIF: Small: Detection and Classification Problems in Online Information Graphs

CIF：小：在线信息图中的检测和分类问题

• 批准号: 1422193
• 负责人: Rohit Negi
• 金额: $ 40.33万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422193&HistoricalAwards=false
• 关键词: CIF; Small; Detection; Classification; Problems

Online Social Networks (OSN) such as Facebook, Twitter, as well as a plethora of online datasets, such as Wikipedia and US government?s DATA.GOV initiative are becoming the repositories of knowledge and discussion for the future. These OSNs and online datasets all share the common feature that they can be thought of as ?Online Information Graphs? (OIG), in the sense that the information embedded in them has a natural graph structure, such as Facebook?s graph of ?friends?. This project considers the question of detection and classification of real-world events using these Online Information Graphs from a communication theoretic viewpoint, by using the graph to specify a Bayes prior. As more and more data is made freely available on the internet, utilizing it to extract information becomes important for society, which is the main goal of the project. For example, Open Government data can improve governance, if inferences applicable to society can be made from it.The questions about OIG that are being investigated are; defining and learning suitable models for the Bayes prior of the OIG, defining various detection and classification problems on OIGs, designing detection algorithms for large OIGs, analyzing detector performance, and engineering the information graph to improve detection. The research is partly experimental (e.g., exploring real-world datasets) and partly theoretical (e.g., analyzing detector performance), and so, intellectual contributions will be made in both categories. Algorithms are being designed to operate on these models, for graph structures typical in OSN-type datasets. The research uses analytical techniques from communication theory, adapted to probabilistic graphical models, to predict error performance and to engineer the networks.

在线社交网络（OSN），如Facebook，Twitter，以及大量的在线数据集，如维基百科和美国政府？GOV倡议正在成为知识和讨论的未来仓库。这些OSN和在线数据集都有一个共同的特征，即它们可以被认为是？在线信息图表？(OIG)，从某种意义上说，嵌入其中的信息具有自然的图形结构，比如Facebook？的图？朋友？该项目考虑了使用这些在线信息图从通信理论的角度来检测和分类现实世界中的事件的问题，通过使用该图来指定贝叶斯先验。随着越来越多的数据在互联网上免费提供，利用它来提取信息对社会变得重要，这是该项目的主要目标。例如，如果可以从开放式政府数据中得出适用于社会的推论，则开放式政府数据可以改善治理。正在研究的关于OIG的问题是：定义和学习OIG的贝叶斯先验的合适模型，定义OIG上的各种检测和分类问题，设计大型OIG的检测算法，分析检测器性能，以及设计信息图以改善检测。该研究部分是实验性的（例如，探索真实世界数据集）和部分理论（例如，分析探测器性能），因此，将在这两个类别中做出智力贡献。正在设计算法来操作这些模型，用于OSN类型数据集中典型的图形结构。该研究使用来自通信理论的分析技术，适用于概率图形模型，以预测错误性能并设计网络。