EAGER: FODAVA: Spectral Analysis for Fraud Detection in Large-scale Networks

EAGER：FODAVA：大规模网络中欺诈检测的频谱分析

• 批准号: 1047621
• 负责人: Xintao Wu
• 金额: $ 10万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1047621&HistoricalAwards=false
• 关键词: EAGER; FODAVA; Spectral; Analysis; Fraud

AbstractThis project takes a unified spectral transformation approach to address challenges of analyzing network topology and identifying fraud patterns in large-scale dynamic networks by using data spectral transformation with network topology visualization. Large-scale social and communication networks contain rich topological information embedded inside, in addition to various structured, semi-structured, and unstructured data. There has been little to date work dedicated to exploring network topology, especially from the spectral analysis point of view. If the proposed methods, which are based on the simple adjacency matrix representation of a graph and the node representation based on k communities in the graph, can be demonstrated to work for very large data sets, it will be a significant advance. The research is being integrated with information visualization and visual analytics algorithms and has a testbed of banking data available to allow for a search for fraud. The research is characterizing patterns of various attacks in the spectral projection space and developing spectrum based methods to identify these attacks. The approach, which exploits the spectral space of the underlying interaction structure of the network, is orthogonal to traditional approaches using content profiling. The ability to perform this spectral analysis is dependent upon the development of complex mathematical techniques. Critical issues that are being explored include the scalability of the methods to very large data sets and the determination of the dimensionality of the node representation in spectral space (which depends upon the number of clusters in the graph). Another issue is that each component of the k-dimensional representation of each node is interpreted as the 'likelihood' of a node's attachment to the k communities. However, it must be guaranteed that the components of the k-dimensional vector that represent each node will be all nonnegative or else an interpretation of the negative number as 'likelihood' must be developed that is mathematically consistent. These and other related mathematical issues are being explored.

摘要本项目采用统一的谱变换方法，通过将数据谱变换与网络拓扑可视化相结合来解决大规模动态网络中分析网络拓扑和识别欺诈模式的挑战。除了各种结构化、半结构化和非结构化的数据外，大规模的社交和通信网络中还嵌入了丰富的拓扑信息。到目前为止，致力于探索网络拓扑的工作很少，特别是从频谱分析的角度。如果所提出的基于图的简单邻接矩阵表示和基于图中k个社区的节点表示的方法能够被证明适用于非常大的数据集，这将是一个重大的进步。这项研究正在与信息可视化和视觉分析算法相结合，并有一个银行数据试验台，可以用来搜索欺诈行为。该研究正在谱投影空间中表征各种攻击的模式，并开发基于谱的方法来识别这些攻击。该方法利用了网络底层交互结构的频谱空间，与使用内容分析的传统方法是正交的。进行这种光谱分析的能力取决于复杂数学技术的发展。正在探索的关键问题包括方法对非常大的数据集的可伸缩性以及谱空间中节点表示的维度的确定(这取决于图中的簇的数量)。另一个问题是，每个节点的k维表示的每个分量被解释为节点依附于k个社区的‘可能性’。然而，必须保证表示每个节点的k维向量的分量都是非负的，否则必须开发出在数学上一致的将负数解释为“可能性”的方法。这些和其他相关的数学问题正在探索中。