社会网络异常事件的检测方法及数据处理技术研究

项目名称： 社会网络异常事件的检测方法及数据处理技术研究

项目编号： No.61472022

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 其他

项目作者： 李建欣

作者单位： 北京航空航天大学

项目金额： 86万元

中文摘要： 社会网络大数据反映了人类社会和物理世界的复杂联系，其大规模用户和海量实时数据能够有助于异常事件的检测和分析，具有重要的商业和社会价值。然而由于社交网络的流式数据动态性强、数据量庞大，传统基于突发特征以及话题检测方法难以有效适用。同时，社交网络的实时数据结构上具有图特征，且持续更新，传统批处理方式存在局限性。本项目针对社会网络大数据的异常事件检测需求和技术挑战，提出一套以图结构为数据特征、以增量处理为算法模式的协同检测体系。从数据内容层面，研究基于突发特征增量聚类的异常事件检测模型，以及非参数异构图扫描算法和事件评估方法等，从数据结构层面，研究基于图数据流的异常热点和节点识别方法；从数据处理层面，研究基于分布式内存的图计算框架、优化机制和可靠性保障技术等；最终，研发基于微博的原型系统，并进行技术试验和应用。本项目将有效平衡事件检测准确性与处理效率的关系，有助于大数据计算科学问题的探索。

中文关键词： 社会网络；异常事件检测；数据处理；图计算；增量处理

英文摘要： Data of modern social network services reflects the complex connection between the society and physical world. The scale its users and real-time nature of the data can contribute to the analysis and detection of anomalous events online or offline, which is of great value to both commercial activity and society. However, the social network data stream is highly dynamic and huge in sheer volume. As a result, traditional topic and event detection methods cannot be applied effectively or directly to modern data. Meanwhile, data of the social network services can be naturally modeled as dynamic graphs, which poses new challenge and problems for traditional batch processing techniques. We identify the requirements and challenges posed by the problem of anomalous event detection over modern social network data streams and propose a monitoring framework based on incremental computing algorithms, exploiting the graph nature of the data. In terms of the content of the data, we would research on anomalous event detection models based on bursty features, the application of non-parametric heterogeneous graph scan techniques and evaluation methods of the detected events. In terms of data structure, we would study anomalous hot spot detection in graph steams and identify nodes with anomalous network structure. In terms of data processing, we would build a distributed graph computing system based on in-memory computing and develop optimization and fault tolerance mechanisms. In the end we would integrate the proposed technologies and build a prototype system for research, experiment and real-life applications. Our project is expected to complement the theories of event detection, provide knowledge on balancing detection quality and processing efficiency and contribute to the understanding, exploration and application of the big data computing.

英文关键词： Social Network;Anomalous Event Detection;Data Processing;Graph Computing;Incremental Processing