III: Small: Collaborative Research: Anomaly Detection in Graph Streams

该项目的主要目标是开发可扩展的算法，用于学习图形流中的规范模式和异常，其中模式是已知的，未知的但固定的，或者随着时间的推移而变化。项目团队正在追求几种技术，包括随时间划分图，只处理随时间对图的更改，以及在高性能计算平台上并行实现。他们正在使用几个真实世界的大型动态数据集和合成数据，从预期数据大小、数据速率和召回率/精度方面评估这些算法的有效性和效率。他们还评估发现的模式和异常在目标领域的重要性。这项研究正在推进如何有效地处理以图形表示的大型、高速率数据流的知识和理解，以便实时学习结构模式和检测结构异常。在这个项目下开发的算法代表了一个新的可扩展性水平，这是当今解决问题所必需的。S海量、动态的数据环境，以及用户以趋势和异常形式快速发现可操作情报的需求。该项目通过推进在大型动态数据流中挖掘模式和异常图形的最新技术，并通过出版物、软件工具和项目网站上提供的数据传播这些研究结果，影响了科学研究界。该项目还通过将研究成果纳入团队所在机构的现有课程，并通过项目网站传播这些课程材料，从而影响教育。该项目支持两名研究生的研究培训，利用从代表性不足的群体中招募的努力来协助选择这些学生。该项目通过提供高效和有效的工具来检测数据中的模式和异常，从而使社会受益，这些工具可以在大量动态数据可用的各种领域（包括国家安全、网络安全和社交媒体）中获得新发现。欲了解更多信息，请参阅项目网站：http://ailab.wsu.edu/adgs

The main objective of this project is to develop scalable algorithms for learning normative patterns and anomalies in graph streams, where the patterns are known, unknown but fixed, or changing over time. The project team is pursuing several techniques, including partitioning the graph over time, processing only the changes to the graph over time, and parallel implementations on high-performance computing platforms. They are evaluating the effectiveness and efficiency of these algorithms in terms of expected data sizes, data rates, and recall/precision using several real-world, large, dynamic datasets as well as synthetic data. They are also evaluating the discovered patterns and anomalies for their significance in the target domains. This research is advancing the knowledge and understanding of how to efficiently process large, high-rate data streams represented as a graph in order to learn structural patterns and detect structural anomalies in real time. The algorithms developed under this project represent a new level of scalability that is necessary to address today?s massive, dynamic data environments, as well as users' needs to quickly discover actionable intelligence in the form of trends and anomalies. This project impacts the scientific research community by advancing the state-of-the-art in mining graphs for patterns and anomalies in large, dynamic data streams, and disseminating these research results via publications, software tools and data to be provided on the project website. This project also impacts education via the inclusion of research results into existing courses at the teams' institutions, and the dissemination of these curricular materials via the project website. The project supports the research training of two graduate students, utilizing recruiting efforts from underrepresented groups to assist in the selection of these students. The project benefits society by providing efficient and effective tools for detecting patterns and anomalies in data that can lead to new discoveries in a variety of domains where large amounts of dynamic data are available, including national security, cyber-security, and social media.For further information see project website: http://ailab.wsu.edu/adgs