Social Network Size in Humans

Durham University, Durham, England, United Kingdom
Human Nature (Impact Factor: 1.96). 04/2003; 14(1):53-72. DOI: 10.1007/s12110-003-1016-y


This paper examines social network size in contemporary Western society based on the exchange of Christmas cards. Maximum
network size averaged 153.5 individuals, with a mean network size of 124.9 for those individuals explicitly contacted; these
values are remarkably close to the group size of 150 predicted for humans on the basis of the size of their neocortex. Age,
household type, and the relationship to the individual influence network structure, although the proportion of kin remained
relatively constant at around 21%. Frequency of contact between network members was primarily determined by two classes of
variable: passive factors (distance, work colleague, overseas) and active factors (emotional closeness, genetic relatedness).
Controlling for the influence of passive factors on contact rates allowed the hierarchical structure of human social groups
to be delimited. These findings suggest that there may be cognitive constraints on network size.

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    • "Another potential complication has to do with the size of some criminal networks[109,135]. The natural size of a human social network is % 150[136], or in more specific terms, "[m]aximum network size averaged 153.5 individuals, with a mean network size of 124.9 for those individuals explicitly contacted " ([137][p. 53]). In criminal networks, however, with the exception of certain cases such as the Italian Mafia[108]or corrupt companies such as ENRON[138], the number of members may be relatively limited[139]. "
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    ABSTRACT: Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers-Logistic Regression, Naïve Bayes and Random Forest-with a range of social network measures and the necessary databases to model the verdicts in two real-world cases: the U.S. Watergate Conspiracy of the 1970's and the now-defunct Canada-based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures.
    Full-text · Article · Jan 2016 · PLoS ONE
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    • "Note, however, that there is no equivalent age effect for the inner two layers of the network (electronic supplementary material, tables S5 and S7), suggesting that the age effect applies only in the outermost layer(s) of more casual friendships. Even so, this age effect contrasts with findings from offline networks, where younger respondents tend to have significantly smaller social networks than older adults[21,24,35]. A likely explanation for this difference probably lies in the fact that SNSs typically encourage promiscuous 'friending' of individuals who often have very tenuous links to ego (X is a friend [or friend-of-friend-of-a-friend] of Y, so would you like to befriend them?). "

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    • "In addition, other studies have demonstrated that there is a significant difference between traditional offline social networks and online social networks. In traditional social networks, an individual has about 10-20 close relationships (Parks, 2007) and manages up to about 125 social relationships (Hill & Dunbar, 2003). In comparison, individuals in online network systems frequently accrue friends numbering several hundred (Tong, Van Der Heide, & Langwell, 2008). "
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    ABSTRACT: Nowadays, millions of people use social network sites (SNSs) to communicate with each other, but little is known about the real effects that online popularity (i.e., the number of friends on a SNS) has on users’ behaviors. This paper explores the social influence of SNSs and demonstrates that the number of online friends on an SNS does not influence its users’ purchasing and lifestyle choices. This study also reveals that so-called low-popular users (i.e., users with few friends on a SNS) are influenced by the intensity of their perceived friendships (i.e., how strong they perceive their relations with their online friends). On the contrary, high-popular users (i.e., users with many friends on a SNS) are influenced by their online friends’ perceived coolness (i.e., how “cool” they consider their online friends), and, in particular, their influence on purchasing decisions increases with the value of the products that they intend to buy. Results shed light on a new meaning of the term “friendship” on a SNS, which is substantially different from what is common in offline contexts: this new construct, which we call “Friendoolness”, can be intended as a mix of friendship and coolness (i.e., social attractiveness, likeability and desirability) and it is mainly based on taking actions to demonstrate that a person has a large number of “cool” friends.
    Full-text · Article · Mar 2015 · Journal of Media Business Studies
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