越览（133）——精读博士论文的2.1社会网络理论

摘要：This issue of tweets will introduce 2.1 Social network theory of the doctoral thesis "Research on Industrial Chain Resilience from

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“越览（133）——精读博士论文

《供应链网络结构视角下的产业链韧性研究》

的2.1社会网络理论

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Today, the editor brings you

2.1 Social network theory

of "Yuelan (133) —— Intensive reading of

the doctoral dissertation

‘Research on the resilience of

thesupplychain from the perspective of

supply chain network structure’".

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一、内容摘要（Summary of content）

本期推文将从思维导图、精读内容、知识补充三个方面介绍博士论文《供应链网络结构视角下的产业链韧性研究》的2.1社会网络理论。

This issue of tweets will introduce 2.1 Social network theory of the doctoral thesis "Research on Industrial Chain Resilience from the Perspective of Supply Chain Network Structure" from three aspects: mind mapping, intensive reading content, and knowledge supplement.

二、思维导图（Mind mapping）

三、精读内容（Intensive reading content）

（一）社会网络理论的起源（The origins of social network theory）

现代社会网络分析（SNA）起源于20世纪30年代，由雅各布·莫雷诺等学者发展为社会计量学。40年代，麻省理工学院的研究团队通过分析沟通网络对群体问题解决能力的影响，强调了网络结构的重要性，激发了多学科的兴趣。到了80年代，社会网络分析成为社会科学的热门理论。

Modern social network analysis (SNA) originated in the 1930s and was developed into sociometrics by scholars such as Jacob Moreno. In the 1940s, a research team at the Massachusetts Institute of Technology emphasized the importance of network structure by analyzing the impact of communication networks on group problem-solving ability, which stimulated the interest of multiple disciplines. In the 1980s, social network analysis became a popular theory in social sciences.

社会网络理论的早期发展包括社会计量分析、人际关系研究和人类学研究。60年代，理论框架逐渐完善，并通过块建模和多维缩放等技术推进。如今，社会网络理论已成为解释社会行为的重要工具，适用于微观（如领导力、团队等）和宏观（如企业关系、组织联盟等）层面的分析。

Early developments in social network theory included sociometric analysis, interpersonal relations research, and anthropological studies. In the 1960s, the theoretical framework was gradually refined and advanced through techniques such as block modeling and multidimensional scaling. Today, social network theory has become an important tool for explaining social behavior, applicable to micro (such as leadership, teams, etc.) and macro (such as corporate relationships, organizational alliances, etc.) level analysis.

（二）社会网络理论研究方法（Research methods of social network theory）

社会网络理论主要利用一些结构指标对网络的特征进行描绘。整体网络的分析指标包括：

Social network theory mainly uses some structural indicators to describe the characteristics of the network. The analysis indicators of the overall network include:

1. 网络密度：衡量网络关系的紧密程度，密度越接近1，关系越密集。密度过高可能导致过多的节点依赖，而过低则可能导致缺乏交流。

1. Network density: measures the closeness of network relationships. The closer the density is to 1, the denser the relationships are. Too high a density may lead to too many node dependencies, while too low a density may lead to a lack of communication.

2. 网络直径：反映网络中任意两节点之间的最长距离，减少网络直径可以提高信息流动和资源传递效率。

2. Network diameter: reflects the longest distance between any two nodes in the network. Reducing the network diameter can improve the efficiency of information flow and resource transfer.

3. 凝聚子群：用于分析网络中的小团体或集群，表示在特定条件下，节点之间具有紧密的互动关系。

3. Cohesive subgroups: used to analyze small groups or clusters in the network, indicating that under certain conditions, there is a close interactive relationship between nodes.

个体网络的结构分析指标包括：

The structural analysis indicators of individual networks include:

1. 心性：反映节点与其他节点的直接连接数，连接越多，节点的权力越大。分为绝对点度中心性（节点的度数）和相对点度中心性（标准化度数）。

1. Centrality: reflects the number of direct connections between a node and other nodes. The more connections, the greater the power of the node. It is divided into absolute degree centrality (the degree of the node) and relative degree centrality (normalized degree).

2. 介数中心性：测量节点在网络中作为“中介”的角色，节点位于多条路径上时，控制资源的能力较强。

2. Betweenness centrality: measures the role of a node as an “intermediary” in the network. When a node is located on multiple paths, it has a stronger ability to control resources.

3. 接近中心性：反映节点与其他节点的接近程度，节点与其他节点的距离越小，接近中心性越高。

3. Closeness centrality: reflects the closeness of a node to other nodes. The smaller the distance between a node and other nodes, the higher the closeness centrality.

4 特征向量中心性：衡量节点的重要性，节点的影响力受到其相连节点影响，核心节点具有较高的特征向量中心性。

4. Eigenvector centrality: It measures the importance of a node. The influence of a node is affected by its connected nodes. Core nodes have higher eigenvector centrality.

（三）社会网络理论与产业链研究（Social network theory and industrial chain research）

社会网络理论在各学科领域具有重要指导作用，广泛应用于计算机科学、数学、教育、神经科学、经济学等领域。通过社会网络分析方法，学者们研究了许多社会现象，如合著者网络和引文网络。社会网络理论同样对产业链网络研究具有重要影响，产业链中的企业及其相互关系构成了一个有机的网络系统。产业链研究的关键是对网络结构特征的刻画，社会网络分析是研究网络结构的重要方法之一。

Social network theory plays an important guiding role in various disciplines and is widely used in computer science, mathematics, education, neuroscience, economics and other fields. Through social network analysis methods, scholars have studied many social phenomena, such as co-author networks and citation networks. Social network theory also has an important impact on the study of industrial chain networks. Enterprises in the industrial chain and their mutual relationships constitute an organic network system. The key to industrial chain research is to characterize the characteristics of network structure. Social network analysis is one of the important methods to study network structure.

四、知识补充（Knowledge supplement）

鲁棒性是指一个系统、模型或方法在面对不确定性、噪声、异常输入或环境变化时，仍能保持其预期功能和性能的能力。换句话说，鲁棒性衡量的是系统在非理想条件下的稳定性和可靠性。在工程、统计学、机器学习和控制理论等领域，鲁棒性是一个关键指标。

Robustness refers to the ability of a system, model, or method to maintain its intended functionality and performance in the face of uncertainty, noise, abnormal inputs, or environmental changes. In other words, robustness measures the stability and reliability of a system under non-ideal conditions. Robustness is a key metric in fields such as engineering, statistics, machine learning, and control theory.

例如，在机器学习中，一个鲁棒的分类模型即使在训练数据存在噪声或测试数据分布与训练数据略有不同时，仍能保持较高的预测准确率。类似地，在自动控制系统中，鲁棒性意味着即使传感器数据存在误差或外部环境发生扰动，系统仍能稳定运行而不失控。鲁棒性通常通过优化算法设计（如正则化）、增加冗余或采用容错机制来实现。

For example, in machine learning, a robust classification model can maintain high prediction accuracy even when there is noise in the training data or the distribution of the test data is slightly different from the training data. Similarly, in automatic control systems, robustness means that the system can still operate stably without losing control even if there are errors in the sensor data or disturbances in the external environment. Robustness is usually achieved by optimizing the algorithm design (such as regularization), adding redundancy, or adopting fault-tolerant mechanisms.

风险鲁棒性是鲁棒性概念在风险管理领域的延伸，特指在面对极端不确定性或罕见但高影响（即“尾部风险”）事件时，系统或决策过程能够有效防范重大损失的能力。与一般鲁棒性不同，风险鲁棒性更关注低概率、高破坏性的事件，例如金融市场崩盘、供应链断裂或自然灾害。

Risk robustness is an extension of the concept of robustness in the field of risk management. It specifically refers to the ability of a system or decision-making process to effectively prevent significant losses when faced with extreme uncertainty or rare but high-impact events (i.e., "tail risk"). Unlike general robustness, risk robustness focuses more on low-probability, high-destructive events, such as financial market collapse, supply chain disruptions, or natural disasters.

在金融领域，风险鲁棒性可能涉及构建抗冲击的投资组合，使其在市场剧烈波动时仍能保持稳定。在供应链管理中，企业可能通过多元化供应商或建立应急库存来增强风险鲁棒性。风险鲁棒性的核心思想是在不确定性下优化最坏情况的表现，而不仅仅是应对一般的随机扰动。常用的方法包括鲁棒优化、压力测试和情景分析等。

In finance, risk robustness may involve building shock-resistant portfolios that remain stable during market fluctuations. In supply chain management, companies may enhance risk robustness by diversifying suppliers or building emergency inventory. The core idea of risk robustness is to optimize the worst-case performance under uncertainty, rather than just responding to general random disturbances. Commonly used methods include robust optimization, stress testing, and scenario analysis.

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