期刊文献+

社会网络模型研究论析 被引量:87

Analysis and Discussion on the Model of Social Network
原文传递
导出
摘要 Social network analysis is explicitly interested in the relationships among social actors. Focusing on structural variables, it opens up a field of data analysis and model building which is completely different from conventional social statistical methods. Spanning nearly seventy years of research, statistical network analysis has witnessed three stages of models. Beginning from the late 1930s, the first generation of scholars (Moreno, Katz, Heider, etc.) studied the distribution of various network statistics. The second stage began from the 1970s and continued to the mid 1980s. It dealt primarily with exponential family of probability distributions for directed graphs (p 1 model) under the vital assumption of “dyad independence”. Relaxing this assumption, Frank and Strauss (1986), Strauss and Ikeda (1990), Wasserman and Pattison (1996) published their pathbreaking papers based on Markov’s random graphs models (p\+* model and its generalization: logit p\+*), which brought social network models to a new stage. It is an extremely flexible and complete model dealing with all sorts of structural aspects of social networks. This substantial “real” structural research should be employed to examine the relational essence of Chinese society. Social network analysis is explicitly interested in the relationships among social actors. Focusing on structural variables, it opens up a field of data analysis and model building which is completely different from conventional social statistical methods. Spanning nearly seventy years of research, statistical network analysis has witnessed three stages of models. Beginning from the late 1930s, the first generation of scholars (Moreno, Katz, Heider, etc.) studied the distribution of various network statistics. The second stage began from the 1970s and continued to the mid 1980s. It dealt primarily with exponential family of probability distributions for directed graphs (p 1 model) under the vital assumption of “dyad independence”. Relaxing this assumption, Frank and Strauss (1986), Strauss and Ikeda (1990), Wasserman and Pattison (1996) published their pathbreaking papers based on Markov's random graphs models (p\+* model and its generalization: logit p\+*), which brought social network models to a new stage. It is an extremely flexible and complete model dealing with all sorts of structural aspects of social networks. This substantial “real” structural research should be employed to examine the relational essence of Chinese society.
作者 刘军
出处 《社会学研究》 CSSCI 北大核心 2004年第1期1-12,共12页 Sociological Studies
  • 相关文献

参考文献42

  • 1Anderson, C. J., Wasserman, S. & Crouch, B. 1999, "A p" Primer: Logit Models for Social Networks." Social Networks 21. 被引量:1
  • 2Boek, R.D. & Husain, S.Z. 1952, "Factors of the Tele: A Preliminary Report." Sociometry 15. 被引量:1
  • 3Breiger, R. L. 1976, "Career Attributes and Network Atructure:A Blockmodel Study of A Biomedical Research Specially." American Sociological Review 41. 被引量:1
  • 4Bronfenbrenner,U. 1943 ,"A Constant Frame of Reference for Sociometric Research. "Sociometry 6. 被引量:1
  • 51944, "A Constant Frame of Reference for Sociometric Research, Part Ⅱ : Experiment and Inference " Sociometry 7. 被引量:1
  • 6Burr, R. S. 1982, Toward A Structural Theory of Action, Academic Press. 被引量:1
  • 7Davis, J. A. 1970, "Clustering and Hierarchy in Interpersonal Relations: Testing Two Theoretical Models on 742 Sociograms." American Sociological Review 35. 被引量:1
  • 8Everett, M. 2002, Social Network Analysis, Textbook at Essex Summer School. 被引量:1
  • 9Faust, K.& Skvoretz, J. 1999, "Logic Models for Affiliation Networks." In Sociological Methodology, (ed.) by Michael Sobel & Mark Becker, New York: Blackwell. 被引量:1
  • 10Fienberg, S. E. & Wasserman, S. 1981, "Categorical Data Analysis of Single Sociometric Relations." in Leinhardt, S. (ed.) Sociological Methodology, San Francisco : Jossey-Bass. 被引量:1

同被引文献1329

引证文献87

二级引证文献490

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部