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Implementation of a Software System for Patterns Identification in a Multigraph Exemplified by an Interbank Lending Network in the Agent Based Model of a Banking System

机译:基于银行代理模型的银行间借贷网络示例的多图中模式识别软件系统的实现

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A graph is a universal representation of interrelations characterizing different social and economic phenomena. Vertices of a graph represent objects of different kinds and its edges describe interrelations between these objects. Vertices and edges can be characterized by corresponding sets of attributes describing different properties of objects and relations between them. A graph is called a multigraph if its elements are characterized by multiple attributes. In such a case, different phenomena taking place in a system can be described in terms of multigraph's subgraph, or pattern, having particular structure. Then the problem of phenomena (pattern) recognition can be formalized as a problem of subgraph matching. The study presents the software aimed at solution of the problem of subgraph matching in multigraphs. The software is implemented in the multi-agent framework. One of its key structural elements is a knowledge base containing quantitative description of patterns to be matched. The software system contains a set of program agents responsible for searching for the particular patterns. As subgraph matching problem is NP-complete, there is no universal efficient algorithm for matching a pattern having arbitrary structure. Therefore, different search agents may use different algorithms. As a result, the multi-agent architecture is necessary for efficient system implementation usage. In the study, the system operation is exemplified by the search for two different patterns in the interbank network generated by the agent based model of banking system. The first pattern corresponds to the appearance of the Ponzi scheme, and the second one corresponds to the case of cyclical transition of money between three banks. The analysis of dependence between the efficiency of pattern recognition, and the amount of noise in the data is conducted.
机译:图是表征各种社会和经济现象的相互关系的通用表示。图的顶点表示不同种类的对象,其边缘描述这些对象之间的相互关系。顶点和边缘可以通过描述对象不同属性及其之间关系的对应属性集来表征。如果图的元素具有多个属性,则该图称为多图。在这种情况下,可以根据具有特定结构的多图子图或图案来描述系统中发生的不同现象。然后可以将现象(模式)识别问题形式化为子图匹配问题。该研究提出了旨在解决多图子图匹配问题的软件。该软件在多代理框架中实现。它的主要结构要素之一是知识库,其中包含要匹配模式的定量描述。该软件系统包含一组负责搜索特定模式的程序代理。由于子图匹配问题是NP完全的,因此没有通用的高效算法来匹配具有任意结构的模式。因此,不同的搜索代理可以使用不同的算法。结果,多代理体系结构对于有效的系统实现使用是必需的。在研究中,通过在基于代理的银行系统模型生成的银行间网络中搜索两种不同的模式来举例说明系统操作。第一种模式对应于庞氏骗局的出现,第二种模式对应于三家银行之间货币周期性过渡的情况。进行模式识别效率与数据中噪声量之间的相关性分析。

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