从隶属网络中识别位置：保留人与事件的二元性。
Identifying positions from affiliation networks: Preserving the duality of people and events.
作者信息
Field Sam, Frank Kenneth A, Schiller Kathryn, Riegle-Crumb Catherine, Muller Chandra
机构信息
University of Texas, Austin, USA.
出版信息
Soc Networks. 2006;28(2):97-123. doi: 10.1016/j.socnet.2005.04.005.
Frank's [Frank, K.A., 1995. Identifying cohesive subgroups. Social Networks 17, 27-56] clustering technique for one-mode social network data is adapted to identify positions in affiliation networks by drawing on recent extensions of p(*) models to two-mode data. The algorithm is applied to the classic Deep South data on southern women and the social events in which they participated with results comparable to other algorithms. Monte Carlo simulations are used to generate sampling distributions to test for the presence of clustering in new data sets and to evaluate the performance of the algorithm. The algorithm and simulation results are then applied to high school students' transcripts from one school from the Adolescent Health and Academic Achievement (AHAA) extension of the National Longitudinal Study of Adolescent Health.
弗兰克[弗兰克，K.A.，1995年。识别凝聚子群。《社会网络》17，27 - 56]针对单模社会网络数据的聚类技术，通过借鉴p（*）模型对双模数据的最新扩展，被用于识别隶属网络中的位置。该算法应用于关于南方女性及其参与的社会活动的经典“深南”数据，结果与其他算法相当。蒙特卡罗模拟用于生成抽样分布，以测试新数据集中聚类的存在情况，并评估该算法的性能。然后，该算法和模拟结果应用于全国青少年健康纵向研究青少年健康与学业成就（AHAA）扩展项目中一所学校的高中生成绩单。
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