# Composition and structure of social networks

Mathématiques et Sciences Humaines (1997)

- Volume: 137, page 11-23
- ISSN: 0987-6936

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topFrank, Ove. "Composition and structure of social networks." Mathématiques et Sciences Humaines 137 (1997): 11-23. <http://eudml.org/doc/94492>.

@article{Frank1997,

abstract = {Social networks representing one or more relationships between individuals and one or more categorical characteristics of the individuals exhibit both structure and composition. Probabilistic models of such networks can be used for analyzing the interrelations between structural and compositional variables, for instance in order to find how structure can be explained by composition or how structure explains composition. Different models are discussed and different statistical methods are employed to illustrate such interrelationships in network data.},

author = {Frank, Ove},

journal = {Mathématiques et Sciences Humaines},

keywords = {Markov dyad models; conditional independence models},

language = {eng},

pages = {11-23},

publisher = {Ecole des hautes-études en sciences sociales},

title = {Composition and structure of social networks},

url = {http://eudml.org/doc/94492},

volume = {137},

year = {1997},

}

TY - JOUR

AU - Frank, Ove

TI - Composition and structure of social networks

JO - Mathématiques et Sciences Humaines

PY - 1997

PB - Ecole des hautes-études en sciences sociales

VL - 137

SP - 11

EP - 23

AB - Social networks representing one or more relationships between individuals and one or more categorical characteristics of the individuals exhibit both structure and composition. Probabilistic models of such networks can be used for analyzing the interrelations between structural and compositional variables, for instance in order to find how structure can be explained by composition or how structure explains composition. Different models are discussed and different statistical methods are employed to illustrate such interrelationships in network data.

LA - eng

KW - Markov dyad models; conditional independence models

UR - http://eudml.org/doc/94492

ER -

## References

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