Multiobjective blockmodeling for social network analysis.
Multiobjective blockmodeling for social network analysis.
复制标题
用于社交网络分析的多目标块建模。
DOI：
10.1007/s11336-012-9313-1
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发表时间：
2013
期刊：
影响因子：
3
通讯作者：
Satornino,CinthiaB
中科院分区：
文献类型：
--
作者：
Brusco,Michael;Doreian,Patrick;Steinley,Douglas;Satornino,CinthiaB
到目前为止，大多数用于社交网络数据的直接块建模的方法都集中在单个目标函数的优化上。然而，存在各种社交网络应用，其中同时考虑两个或更多个目标是有利的。这些应用程序大致可分为两类：（1）同时优化用于基于单个网络矩阵拟合块模型的多个标准，以及（2）同时优化用于基于两个或更多个网络矩阵拟合块模型的多个标准，其中被拟合的矩阵可以采取用于基础关系的多个指示符的形式，或者用于在两个或多个不同时间点测量的一组对象的多个矩阵。提出了一种多目标禁忌搜索算法来估计Pareto有效块模型集。这个过程中使用的三个例子，演示了可能的应用程序的多目标块建模范例。
To date, most methods for direct blockmodeling of social network data have focused on the optimization of a single objective function. However, there are a variety of social network applications where it is advantageous to consider two or more objectives simultaneously. These applications can broadly be placed into two categories: (1) simultaneous optimization of multiple criteria for fitting a blockmodel based on a single network matrix and (2) simultaneous optimization of multiple criteria for fitting a blockmodel based on two or more network matrices, where the matrices being fit can take the form of multiple indicators for an underlying relationship, or multiple matrices for a set of objects measured at two or more different points in time. A multiobjective tabu search procedure is proposed for estimating the set of Pareto efficient blockmodels. This procedure is used in three examples that demonstrate possible applications of the multiobjective blockmodeling paradigm.