Finding Patterns and Anomalies in Large Time-Evolving Graphs

社交网络可以被表示为时间演进图，其中节点是社交网络的成员/实体，并且边表示节点之间的连接/关系。这个项目试图回答这样的问题：一个“正常”的社交网络是什么样子的？随着时间的推移，它将如何演变？我们如何发现“异常”的相互作用（例如，垃圾邮件），在一个随时间演变的电子邮件图？本计画所开发的方法是寻找时间演化的“法则”，并设计快速、可扩充的资料探勘工具，以处理具有数百万甚至数十亿节点的真实的图。 该方法包括两个方面：（1）发现图形随时间演变时保持的模式;（2）分析、可视化和挖掘这些图形以发现异常的工具。 由此产生的工具将具有广泛的适用性。它们将在许多环境中对挖掘和异常值检测至关重要，例如洗钱环，互联网上错误配置的路由器，可疑的用户访问数据库记录，基因调控网络中令人惊讶的蛋白质-蛋白质相互作用，以及涉及大规模进化的社交网络的许多应用。该项目的网址（http：//www.cs.cmu.edu/CHRISTOS/PROJECTS/GRAPH-MINING/）提供了更多的信息，并将用于传播成果。

Social networks can be represented as time-evolving graphs where the nodes are the members/entities of the social network and the edges represent connections/relationships between the nodes. This project tries to answer questions such as: How does a "normal" social network look like? How will it evolve over time? How can we spot "abnormal" interactions (e.g., spam), in a time-evolving e-mail graph? The approach developed in this project is to look for time-evolution "laws", and to design fast, scalable data mining tools for real graphs with millions and billions of nodes. The approach consist of two efforts: (1) discovery of patterns that hold when graphs evolve over time and (2) tools to analyze, visualize and mine such graphs to discover anomalies. The resulting tools will have a broad applicability. They will be vital for mining and outlier detection in numerous settings, such as money-laundering rings, mis-configured routers on the Internet, suspicious user accesses to database records, surprising protein-protein interactions in a gene regulatory network, and many applications involving large-scale evolving social networks. The project Web site (http://www.cs.cmu.edu/~christos/PROJECTS/GRAPH-MINING/) provides additional information and will be used for results dissemination.