Searching Cohesive Subgraphs in Big Attributed Graph Data

大属性图数据的可用性为实现数据的大价值带来了巨大的机遇。理解这样大的属性图数据可以找到许多应用，包括健康，科学，工程，商业，环境等。内聚子图是捕获图中潜在属性的关键组件之一，对于图分析至关重要。该项目旨在从结构和属性两个角度，发明有效的内聚子图模型和有效的算法，用于在大的动态属性图中搜索和监控内聚子图。该项目中开发的方法、技术和原型系统可以部署在全国范围内，以促进大图形数据的智能使用。

The availability of big attributed graph data brings great opportunities for realizing big values of data. Making sense of such big attributed graph data finds many applications, including health, science, engineering, business, environment, etc. A cohesive subgraph, one of key components that captures the latent properties in a graph, is essential to graph analysis. This project aims to invent effective models of cohesive subgraphs and efficient algorithms for searching and monitoring cohesive subgraphs in big and dynamic attributed graphs from both structure and attribute perspectives. The methods, techniques, and prototype systems developed in this project can be deployed to facilitate the smart use of big graph data across the nation.