III: Small: MESH: A Hypergraph Analysis Engine for Understanding Large-Scale Social Networks

通过社交网络应用程序进行的在线互动的数量和丰富性的快速增长正在以前所未有的规模创造数据。 这包括有关个人特征以及他们之间的联系和互动的数据。许多现实世界的应用程序具有涉及多个人的复杂的组动态。对这种群体互动的分析有可能彻底改变社会科学，商业和商业领域。这个项目的目标是开发一个新的计算框架，以支持大型社交网络中的群体动态的可扩展分析。 关键思想是显式地对个体群体进行建模，而不是简单地捕捉个体对之间的联系。该项目的更广泛影响将包括对现实世界网络中复杂交互的更丰富分析。它还将通过计算机系统和数据挖掘领域之间的协同作用的课程和研究经验来增强明尼苏达大学计算机科学课程。为了对网络中的群体交互进行建模，该项目将使用超图（图的概括），其中超边表示一个或多个实体之间的关系。超图有可能为许多群体现象提供更高的建模精度，以及更高的存储和计算效率。 该项目将开发一个名为MESH的分析框架：Minnesota Engine for Scalable evolving Hypergraph analysis，该框架将提供算法和系统组件来支持对进化超图的可扩展分析。MESH算法将被设计用于建模和计算与现实世界网络中的群体动力学相关的几个常见数据驱动问题。MESH系统级技术将被设计为以分布式和可扩展的方式支持这些算法的执行。欲了解更多信息，请访问项目网站：http://mesh.cs.umn.edu

Rapid growth in the amount and richness of online interactions through social networking applications is creating data at unprecedented scales. This includes data about individual's characteristics as well as their connections and interactions. Many real-world applications have complex group dynamics involving multiple people. The analysis of such group interactions has the potential to revolutionize social sciences, business, and commerce domains. The goal of this project is to develop a novel computational framework to support scalable analysis of group dynamics in large social networks. The key idea is to explicitly model groups of individuals rather than simply capturing links between pairs of individuals. The broader impacts of this project will consist of enabling richer analysis of complex interactions in real-world networks. It will also enhance the University of Minnesota Computer Science curriculum through courses and research experiences with synergy between the areas of computer systems and data mining.To model group interactions in networks, this project will use hypergraphs, a generalization of graphs, where hyperedges represent relations between one or more entities. Hypergraphs have the potential to provide higher modeling accuracy for many group phenomena, as well as higher storage and computational efficiency, compared to their graph counterparts. This project will develop an analysis framework called MESH: Minnesota Engine for Scalable evolving Hypergraph analysis, that will provide algorithms and system components to support scalable analysis of evolving hypergraphs. The MESH algorithms will be designed to model and compute several common data-driven questions related to group dynamics in real-world networks. The MESH system-level techniques will be designed to support the execution of these algorithms in a distributed and scalable manner.For further information see the project web site at: http://mesh.cs.umn.edu