结构洞中心性:通过战略网络形成评价社会资本

Q1 Mathematics
Faisal Ghaffar, Neil Hurley
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引用次数: 9

摘要

战略网络形成是网络科学的一个分支,它从经济的角度来看待社会网络的创建,考虑到网络中的行为者通过与网络中的其他行为者的联系来形成联系,以便最大化他们获得的某些效用。特别是,Jackson的连接模型,将一个行为者的效用写成了所有其他行为者的总和,这些行为者可以沿着网络中的一条路径到达,其利益值随着路径长度而减少。在本文中,我们感兴趣的是行为者由于其在网络中的位置而保留的“社会资本”。社会资本可以被理解为一种与行动者建立联系的能力,以及一种建立桥梁的能力,这种桥梁连接了原本脱节的行动者。这种桥接效应已经在Kleinberg提出的另一种“结构洞”网络形成游戏中进行了建模。在本文中,我们开发了一种方法,概括了Kleinberg游戏的效用,并将其与连接模型结合起来,创建了一种效用,该效用既可以模拟参与者与社会资本的联系能力,也可以模拟参与者与社会资本的桥梁能力。从这个效用及其相关的形成博弈中,我们得出了一个新的中心性度量,我们称之为“结构洞中心性”,以识别具有高社会资本的行动者。我们通过将其应用于不同类型的网络来分析这一度量,并使用来自不同领域的299个网络的基准数据集评估其与其他中心性度量的相关性。最后,使用数据集中的一个社交网络,我们说明了如何使用参与者的“结构孔中心性剖面”来识别他们对网络的桥接和粘合价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structural hole centrality: evaluating social capital through strategic network formation
Strategic network formation is a branch of network science that takes an economic perspective to the creation of social networks, considering that actors in a network form links in order to maximise some utility that they attain through their connections to other actors in the network. In particular, Jackson’s Connections model, writes an actor’s utility as a sum over all other actors that can be reached along a path in the network of a benefit value that diminishes with the path length. In this paper, we are interested in the “social capital” that an actor retains due to their position in the network. Social capital can be understood as an ability to bond with actors, as well as an ability to form a bridge that connects otherwise disconnected actors. This bridging benefit has previously been modelled in another “structural hole” network formation game, proposed by Kleinberg. In this paper, we develop an approach that generalises the utility of Kleinberg’s game and combines it with that of the Connections model, to create a utility that models both the bonding and bridging capabilities of an actor with social capital. From this utility and its associated formation game, we derive a new centrality measure, which we dub “structural hole centrality”, to identify actors with high social capital. We analyse this measure by applying it to networks of different types, and assessing its correlation to other centrality metrics, using a benchmark dataset of 299 networks, drawn from different domains. Finally, using one social network from the dataset, we illustrate how an actor’s “structural hole centrality profile” can be used to identify their bridging and bonding value to the network.
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来源期刊
Computational Social Networks
Computational Social Networks Mathematics-Modeling and Simulation
自引率
0.00%
发文量
0
审稿时长
13 weeks
期刊介绍: Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.
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