CAREER: Mining structure and dynamics of groups of nodes in real-world networks

职业：挖掘现实网络中节点组的结构和动态

• 批准号: 1149837
• 负责人: Jurij Leskovec
• 金额: $ 54.07万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-01-15 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149837&HistoricalAwards=false
• 关键词: CAREER; Mining; structure; dynamics; groups

Social, technological, information and biological systems can be studied as graphs, where nodes represent entities (i.e., people, websites) and edges represent interactions (friendships, communication). The project aims to analyze and discover explanatory and predictive models of networked systems, such as large groups of people and societies, or large biological and technological systems, in order to understand their structure and make predictions about their global dynamics.The research studies the structure and dynamics of communities of nodes, with the goal to invent novel network community detection methods and build predictive models of behavior of groups of nodes. The proposed research has three main thrusts: (1) Structure and discovery of network communities, (2) Dynamics and "health" of network communities, and (3) Supervised community detection in networks with rich node and edge metadata. The research focuses on harnessing massive network datasets, as certain behaviors and patterns are observable only when the amount of data is large enough. The intellectual focus of the project is on increasing the expressivity of the models to also include rich node and edge metadata and explore the connections between the network structure and the attributes/features of nodes and edges.The education plan provides for rich research experiences and helps students develop the interdisciplinary attitudes and skills needed for this work through courses that look at real-world network problems and data. An integral part of this proposal is public release of datasets and computational tools for analysis of large networks. Additional information about the project including publications, data sets, source code, and educational materials can be accessed through the project website at http://snap.stanford.edu.

社会、技术、信息和生物系统可以作为图来研究，其中节点代表实体（即，人、网站）和边表示交互（友谊、通信）。该项目旨在分析和发现网络化系统的解释性和预测性模型，如大型人群和社会，或大型生物和技术系统，以便了解其结构并对其全球动态进行预测。该研究研究节点社区的结构和动态其目标是发明新颖的网络社区检测方法并建立节点组行为的预测模型。提出的研究有三个主要方面：（1）网络社区的结构和发现，（2）网络社区的动态和“健康”，以及（3）在网络中具有丰富的节点和边缘元数据的监督社区检测。该研究的重点是利用大量的网络数据集，因为只有当数据量足够大时，某些行为和模式才能被观察到。该项目的智力重点是增加模型的表现力，包括丰富的节点和边缘元数据，并探索网络结构与节点和边缘的属性/特征之间的联系。教育计划提供丰富的研究经验，并通过关注现实世界网络问题和数据的课程，帮助学生培养这项工作所需的跨学科态度和技能。该提案的一个组成部分是公开发布用于分析大型网络的数据集和计算工具。有关该项目的其他信息，包括出版物、数据集、源代码和教育材料，可以通过项目网站http://snap.stanford.edu访问。