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EAGER: Harnessing the Power of Graph Data Analytics

EAGER：利用图数据分析的力量

基本信息

批准号：
1747818
负责人：
Weili Wu
金额：
$ 15万
依托单位：
University of Texas at Dallas
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1747818&HistoricalAwards=false
关键词：
EAGER Harnessing Power Graph Data

项目摘要

The rapid growth of user generated content in online social networks forms an abundant source of big graph data. This project will develop efficient techniques for extracting information from such graph data. Specifically, this EAGER project will develop efficient algorithms for the following tasks: (1) machine learning and sentiment analysis, such as large vocabulary conversational speech recognition, multi-label learning, and multi-polarity sentiment analysis, and (2) effector detection and related issues, such as business expansion and rumor blocking. The advanced optimization techniques developed in this project will significantly improve the performance of the existing data mining algorithms and the theoretical study of efficient approximation techniques. In addition, this project will provide an excellent platform for educating students with hands-on experience. To develop the efficient algorithms described, the project will take advantage of recent development in nonlinear combinatorial optimization, and will formulate new methodologies using discrete convex analysis for submodular functions and duality theory for the discrete DC (different of convex) programming, as well as reverse sampling techniques for probabilistic objective functions. These new methodologies will enrich the throughput of machine learning and sentiment analysis. Since nonlinear combinatorial optimization is still in its early stages, exploratory and innovative research on efficient approximation algorithm will be performed based on fresh fundamental theoretical developments. A detailed evaluation will be conducted based on several real world data sets.

在线社交网络中用户生成内容的快速增长形成了丰富的大图数据源。该项目将开发从此类图形数据中提取信息的有效技术。具体来说，这个EAGER项目将为以下任务开发高效的算法：（1）机器学习和情感分析，例如大词汇量会话语音识别、多标签学习和多极性情感分析，以及（2）效应器检测和相关问题，例如业务扩展和谣言拦截。该项目开发的先进优化技术将显着提高现有数据挖掘算法的性能和高效逼近技术的理论研究。此外，该项目将为教育学生提供实践经验的绝佳平台。为了开发所描述的有效算法，该项目将利用非线性组合优化的最新发展，并将使用子模函数的离散凸分析和离散 DC（凸差）规划的对偶理论以及概率目标函数的逆采样技术来制定新的方法。这些新方法将丰富机器学习和情感分析的吞吐量。由于非线性组合优化仍处于早期阶段，因此将基于新的基础理论发展对高效逼近算法进行探索性和创新性研究。将根据几个现实世界的数据集进行详细的评估。