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首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >A Game Theory Inspired Approach to Stable Core Decomposition on Weighted Networks
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A Game Theory Inspired Approach to Stable Core Decomposition on Weighted Networks

机译：基于博弈论的加权网络稳定核心分解方法

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摘要

Meso-scale structural analysis, like core decomposition has uncovered groups of nodes that play important roles in the underlying complex systems. The existing core decomposition approaches generally focus on node properties like degree and strength. The node centric approaches can only capture a limited information about the local neighborhood topology. In the present work, we propose a group density based core analysis approach that overcome the drawbacks of the node centric approaches. The proposed algorithmic approach focuses on weight density, cohesiveness, and stability of a substructure. The method also assigns an unique score to every node that rank the nodes based on their degree of core-ness. To determine the correctness of the proposed method, we propose a synthetic benchmark with planted core structure. A performance test on the null model is carried out using a weighted lattice without core structures. We further test the stability of the approach against random noise. The experimental results prove the superiority of our algorithm over the state-of-the-arts. We finally analyze the core structures of several popular weighted network models and real life weighted networks. The experimental results reveal important node ranking and hierarchical organization of the complex networks, which give us better insight about the underlying systems.
机译：中尺度结构分析（如核心分解）具有未发现的节点组，这些节点在基础复杂系统中扮演重要角色。现有的核心分解方法通常集中在节点属性（例如度和强度）上。以节点为中心的方法只能捕获有关本地邻居拓扑的有限信息。在当前的工作中，我们提出了一种基于组密度的核心分析方法，该方法克服了以节点为中心的方法的缺点。所提出的算法方法着重于子结构的重量密度，内聚性和稳定性。该方法还为根据节点的核心程度对节点进行排名的每个节点分配唯一的分数。为了确定所提出方法的正确性，我们提出了一种带有种植芯结构的综合基准。使用没有核心结构的加权晶格对零模型进行性能测试。我们进一步测试了针对随机噪声的方法的稳定性。实验结果证明了我们的算法优于最新技术的优越性。最后，我们分析了几种流行的加权网络模型和现实生活中的加权网络的核心结构。实验结果揭示了复杂网络的重要节点排名和层次结构，这使我们可以更好地了解底层系统。

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