Reconstructing Topological Properties of Complex Networks Using the Fitness Model
Reconstructing Topological Properties of Complex Networks Using the Fitness Model
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DOI：
10.1007/978-3-319-15168-7_41
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发表时间：
2014-10
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影响因子：
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通讯作者：
G. Cimini;Tiziano Squartini;N. Musmeci;Michelangelo Puliga;A. Gabrielli;D. Garlaschelli;S. Battiston;G. Caldarelli
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文献类型：
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作者：
G. Cimini;Tiziano Squartini;N. Musmeci;Michelangelo Puliga;A. Gabrielli;D. Garlaschelli;S. Battiston;G. Caldarelli
研究复杂社会经济系统的一个主要问题是隐私问题，这可能会严重限制可访问的信息量，迫使在不完整的知识基础上构建模型。本文研究了一种从有限信息出发重构复杂网络全局拓扑性质的新方法。该方法利用节点的固有属性（表示为适应性）和节点的有限子集的连接数的知识，以生成代表真实的系统的指数随机图的集合，并可用于估计其拓扑性质。在这里，我们特别关注于重建通常用于描述网络的最基本属性：链接密度，重复性，聚类。我们测试的基准合成网络和真实的经济和金融系统的方法，发现一个显着的鲁棒性相对于用于校准的节点数量。因此，该方法代表了一个有价值的工具，以获得对隐私保护系统的见解。
A major problem in the study of complex socioeconomic systems is represented by privacy issues—that can put severe limitations on the amount of accessible information, forcing to build models on the basis of incomplete knowledge. In this paper we investigate a novel method to reconstruct global topological properties of a complex network starting from limited information. This method uses the knowledge of an intrinsic property of the nodes (indicated asfitness), and the number of connections of only a limited subset of nodes, in order to generate an ensemble ofexponential random graphsthat are representative of the real systems and that can be used to estimate its topological properties. Here we focus in particular on reconstructing the most basic properties that are commonly used to describe a network: density of links, assortativity, clustering. We test the method on both benchmark synthetic networks and real economic and financial systems, finding a remarkable robustness with respect to the number of nodes used for calibration. The method thus represents a valuable tool for gaining insights on privacy-protected systems.