A Novel Model for the Calculation of Social Capital in Digital Social Networks

数字社交网络中社会资本计算的新模型

• 批准号: 1942700
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1942700
• 关键词: Novel; Model; Calculation; Social; Capital

Social capital defined at its simplest is the value that can be ascribed to the social ties between individuals in a social network, an intuitive concept that manifests in something as simple as a friend being able to recommend a good plumber. However despite its simplicity it has been shown to have wide ranging impact throughout a tremendous number of areas from sociology, to business studies, to political science. Prior to recent work social capital analysis has remained the domain of more traditional sociological studies of smaller populations, however with the advent of large scale computational social network analysis there is the opportunity to leverage its techniques to take social capital analysis to new domains and population sizes not previously possible in reasonable time scales.That accordingly is the purpose of this project, the development of novel social capital analysis techniques capable of working at scale, however also adjusting for the limitations that such a scale brings. Almost uniformly large social networks do not contain the pieces of specific information on their contained nodes which would be more readily captured through a small scale survey. The importance of this information is in differentiating between the forms of social capital, most notable of these being bonding capital resulting from connections between similar people and bridging capital resulting from connections with those who are diverse. Where we lack the information to measure the similarity between individuals we must then attempt to approximate this indirectly. One avenue we have investigated has been the use of community membership as a common property between nodes in a social network. The intuition being that individuals will cluster together in a social network on the basis of some combination of shared attributes and indeed that community membership can be considered an attribute of the structure of the graph itself.Furthermore a computational approach to social network analysis allows us to make use of machine learning to both approximate measures that are too computationally expensive to calculate in graphs of millions of edges yet are potentially valuable for social network analysis such as decay centrality. It is also possible for us to make use of machine learning to further scrutinise the structures in social networks from which social capital results, a subject which has seen much dispute. We seek to examine the effectiveness of combining various predetermined social network measures which might be associated with social capital as well as attempting to use automated feature extraction in order to escape any assumptions made about the structures. There has been a recent number of works focusing on graph feature extraction primarily either basing their approach on fitting a numeric matrix to random walk samples of the network or using a graph neural network in which the network itself is treated as a neural network and over a series of iterations each node aggregates messages from its neighbours, eventually producing a feature representation for each node. The challenge in adapting such approaches to social capital is that it may not be sufficient to embed structure alone, but rather structure and the resources that an individual can access within that structure. Resources, whether they're information such as the quality of the local plumbers or a loan a friend might give you, can be viewed as the hidden underpinning of social capital and what the structures actually give you access to so it is natural that we should want to capture them as part of our model.

社会资本最简单的定义是可归因于社交网络中个体之间的社会联系的价值，这是一个直观的概念，表现为简单的事情，例如朋友能够推荐一个好的水管工。然而，尽管它很简单，但它已被证明在从社会学、商业研究到政治学的众多领域具有广泛的影响。在最近的工作之前，社会资本分析仍然是针对较小人群的更传统的社会学研究领域，然而，随着大规模计算社交网络分析的出现，有机会利用其技术将社会资本分析带到以前在合理的时间范围内不可能实现的新领域和人口规模。因此，这就是该项目的目的，开发能够大规模工作的新型社会资本分析技术，同时也适应这种规模带来的限制。几乎所有大型社交网络都不包含有关其所包含节点的特定信息，而这些信息通过小规模调查更容易捕获。这些信息的重要性在于区分社会资本的形式，其中最值得注意的是由相似的人之间的联系产生的结合资本和由与不同的人的联系产生的桥接资本。当我们缺乏信息来衡量个体之间的相似性时，我们必须尝试间接地近似这一点。我们研究的一种途径是使用社区成员资格作为社交网络中节点之间的公共财产。直觉是，个体将基于共享属性的某种组合聚集在社交网络中，实际上，社区成员资格可以被视为图本身结构的属性。此外，社交网络分析的计算方法使我们能够利用机器学习来近似度量，这些近似度量在数百万条边的图中计算成本太高，但对于社交网络分析（例如衰减中心性）具有潜在价值。我们还可以利用机器学习来进一步审视社会网络中产生社会资本的结构，这是一个备受争议的话题。我们试图检查组合各种可能与社会资本相关的预先确定的社交网络措施的有效性，并尝试使用自动特征提取来逃避对结构所做的任何假设。最近有一些工作主要关注图特征提取，要么基于将数字矩阵拟合到网络的随机游走样本的方法，要么使用图神经网络，其中网络本身被视为神经网络，并且通过一系列迭代，每个节点聚合来自其邻居的消息，最终为每个节点生成特征表示。将这种方法应用于社会资本的挑战在于，仅仅嵌入结构可能还不够，还需要嵌入结构以及个人可以在该结构中访问的资源。资源，无论是当地水管工的质量等信息，还是朋友可能给你的贷款，都可以被视为社会资本的隐藏基础，以及这些结构实际上为你提供的访问权限，因此我们很自然地希望将它们捕获为我们模型的一部分。