Estimating Vertex Measures in Social Networks by Sampling Completions of RDS Trees.
Estimating Vertex Measures in Social Networks by Sampling Completions of RDS Trees.
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DOI：
10.4236/sn.2015.41001
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发表时间：
2015-01-01
期刊：
影响因子：
--
通讯作者：
Wendel T
中科院分区：
文献类型：
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作者：
Khan B;Dombrowski K;Curtis R;Wendel T
本文提出了一种从不完备数据集中获取网络属性的新方法。与数据缺失相关的问题是社交网络分析中众所周知的绊脚石。根据生成树完成估计连通性的方法是专门设计用于解决只有网络的生成树(S)已知的情况，例如通过响应驱动抽样(RDS)获得的情况。该方法使用从度信息得到的重复随机补全，放弃了通常试图获得最终边或顶点花名册的步骤，而是旨在根据生成树本身来概率地估计顶点的网络中心属性。在本文中，我们讨论了缺失数据的问题，并描述了我们的补全方法的协议，最后给出了一个实验的结果，其中ECSTC被用来估计从特征预先知道的图的生成树中估计图相关顶点属性的实验结果。结果表明，ECSTC方法在从有限的数据集中获得个人的以网络为中心的属性方面比研究人员之前假设的更有希望。这种方法与过去的处理缺失数据的策略是一种决裂，过去的策略主要是寻找完成图形的方法，而不是ECSTC的方法，即在不确定最终边集的情况下估计网络属性本身。
This paper presents a new method for obtaining network properties from incomplete data sets. Problems associated with missing data represent well-known stumbling blocks in Social Network Analysis. The method of “estimating connectivity from spanning tree completions” (ECSTC) is specifically designed to address situations where only spanning tree(s) of a network are known, such as those obtained through respondent driven sampling (RDS). Using repeated random completions derived from degree information, this method forgoes the usual step of trying to obtain final edge or vertex rosters, and instead aims to estimate network-centric properties of vertices probabilistically from the spanning trees themselves. In this paper, we discuss the problem of missing data and describe the protocols of our completion method, and finally the results of an experiment where ECSTC was used to estimate graph dependent vertex properties from spanning trees sampled from a graph whose characteristics were known ahead of time. The results show that ECSTC methods hold more promise for obtaining network-centric properties of individuals from a limited set of data than researchers may have previously assumed. Such an approach represents a break with past strategies of working with missing data which have mainly sought means to complete the graph, rather than ECSTC's approach, which is to estimate network properties themselves without deciding on the final edge set.