Current status and progress of research on the application of complex networks in the field of industrial design

doi:10.11996/JG.j.2095-302X.2023061080

Abstract

Abstract:

With the rapid advancement of network science research, complex networks have become one of the research focal points in various fields. Industrial design, as a highly integrated cross-disciplinary domain, is no exception. Complex network theory provides a more in-depth research perspective, bringing brand-new opportunities within this field. To advance research and foster the application of complex networks in industrial design, a review was conducted on related studies at home and abroad. It summarized the research methods and progress, offering further analysis. Firstly, the complex network theory was presented through three aspects: basic concept, basic model, and main research content. Subsequently, it reviewed the application of complex network theory in industrial design in terms of product structural design, product appearance design, and design demand analysis. Finally, taking into account the existing development of relevant research, it provided an analysis and summary of the application of research results of complex network in industrial design and the research needs for node selection and network construction in the actual design process. It also presented and discussed the research hotspots and development trends in complex networks in industrial design.

Key words: complex network, industrial design, structure design, exterior design, requirements analysis

CLC Number:

TP391

DING Man, LI Peng-hui, ZHANG Yi-fei, MA Hong-kun. Current status and progress of research on the application of complex networks in the field of industrial design[J]. Journal of Graphics, 2023, 44(6): 1080-1090.

Figures/Tables 4

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研究内容	研究方法	特点	优势	劣势
节点排序	度中心性算法	根据节点的度数进行排序	计算速度较快，适用于大规模网络	只考虑节点的度，忽略了节点的位置和其他局部特征
	K-shell算法	通过逐步删除度数较低的节点，将网络剥离为若干层，按照层级排序	考虑节点的局部连接性，能够识别出网络中的核心节点	在大规模网络中计算复杂度较高
	LeaderRank算法	基于节点之间的消息传递计算节点的排序值	考虑节点间的相互作用，可较好地捕捉到节点在整个网络中的重要性	计算复杂度较高，需要进行多次迭代
关联发现	Girvan-Newman算法	通过不断切割网络中的边识别社团结构，将网络分割为多个连通子图	对于具有明显社团结构的网络，能够准确地发现社团之间的关联关系	易将网络中的噪声节点划分为社区，对参数的选择较敏感
	Fast-Newman算法	通过估计节点对之间的流量来发现网络中的关联关系	能够发现重叠社区结构，对节点的多重归属性有较好的处理能力	计算复杂度较高，对网络规模和密度敏感
	谱聚类算法	通过对网络的拉普拉斯矩阵进行特征值分解，并基于特征向量进行聚类	能够发现网络中的潜在结构，并支持非线性分割	对于大规模网络计算复杂度较高

研究内容	研究方法	特点	优势	劣势
节点排序	度中心性算法	根据节点的度数进行排序	计算速度较快，适用于大规模网络	只考虑节点的度，忽略了节点的位置和其他局部特征
	K-shell算法	通过逐步删除度数较低的节点，将网络剥离为若干层，按照层级排序	考虑节点的局部连接性，能够识别出网络中的核心节点	在大规模网络中计算复杂度较高
	LeaderRank算法	基于节点之间的消息传递计算节点的排序值	考虑节点间的相互作用，可较好地捕捉到节点在整个网络中的重要性	计算复杂度较高，需要进行多次迭代
关联发现	Girvan-Newman算法	通过不断切割网络中的边识别社团结构，将网络分割为多个连通子图	对于具有明显社团结构的网络，能够准确地发现社团之间的关联关系	易将网络中的噪声节点划分为社区，对参数的选择较敏感
	Fast-Newman算法	通过估计节点对之间的流量来发现网络中的关联关系	能够发现重叠社区结构，对节点的多重归属性有较好的处理能力	计算复杂度较高，对网络规模和密度敏感
	谱聚类算法	通过对网络的拉普拉斯矩阵进行特征值分解，并基于特征向量进行聚类	能够发现网络中的潜在结构，并支持非线性分割	对于大规模网络计算复杂度较高

研究方法	特点	优势	劣势
马尔可夫算法	基于状态转移概率的随机模型，可描述复杂网络中节点之间的转换行为	计算简单快速，可以较好地适应大规模网络	忽略节点之间的相互作用，在描述复杂的网络行为时存在局限性
蒙特卡罗模拟方法	基于随机采样的数值模拟方法，通过模拟大量随机事件近似计算复杂网络的行为特性	可灵活地模拟各种复杂网络结构和行为，并且可以较好地处理非线性关系	需要进行大量的随机采样和计算，对于大规模网络存在较大的计算资源需求
演化博弈理论	基于博弈论的分析方法，通过研究节点之间的策略选择和演化过程揭示复杂网络中节点行为的动态演化规律	能够较好地描述节点之间的竞争、合作和适应性变化等行为，可以对复杂网络的稳定性和动态行为进行深入分析	在描述复杂网络时需要做一些假设和简化，可能无法覆盖所有的行为模式，并在计算上存在一定的困难

研究方法	特点	优势	劣势
马尔可夫算法	基于状态转移概率的随机模型，可描述复杂网络中节点之间的转换行为	计算简单快速，可以较好地适应大规模网络	忽略节点之间的相互作用，在描述复杂的网络行为时存在局限性
蒙特卡罗模拟方法	基于随机采样的数值模拟方法，通过模拟大量随机事件近似计算复杂网络的行为特性	可灵活地模拟各种复杂网络结构和行为，并且可以较好地处理非线性关系	需要进行大量的随机采样和计算，对于大规模网络存在较大的计算资源需求
演化博弈理论	基于博弈论的分析方法，通过研究节点之间的策略选择和演化过程揭示复杂网络中节点行为的动态演化规律	能够较好地描述节点之间的竞争、合作和适应性变化等行为，可以对复杂网络的稳定性和动态行为进行深入分析	在描述复杂网络时需要做一些假设和简化，可能无法覆盖所有的行为模式，并在计算上存在一定的困难