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

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。大规模的社交和通信网络除了包含各种结构化、半结构化和非结构化数据外，还包含丰富的拓扑信息。迄今为止，很少有工作致力于探索网络拓扑结构，特别是从频谱分析的角度来看。如果所提出的方法，这是基于简单的邻接矩阵表示的图和节点表示的基础上，在图中的k社区，可以证明工作非常大的数据集，这将是一个重大的进步。该研究正在与信息可视化和可视化分析算法相结合，并提供了一个银行数据测试平台，以允许搜索欺诈行为。该研究是在频谱投影空间中表征各种攻击的模式，并开发基于频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正交。进行这种光谱分析的能力取决于复杂数学技术的发展。正在探讨的关键问题包括非常大的数据集的方法的可扩展性和光谱空间中的节点表示的维数的确定（这取决于图中的集群的数量）。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附着到k个社区的“可能性”。然而，必须保证代表每个节点的k维向量的分量都是非负的，否则必须将负数解释为“似然”，这在数学上是一致的。这些和其他相关的数学问题正在探索中。

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.