Why social networks are different from other types of networks.

By Mark Newman, Juyong Park. Physical review. E, Statistical, nonlinear, and soft matter physics, 2003

1,446 citations recorded by the source corpus.

We argue that social networks differ from most other types of networks, including technological and biological networks, in two important ways. First, they have nontrivial clustering or network transitivity and second, they show positive correlations, also called assortative mixing, between the degrees of adjacent vertices. Social networks are often divided into groups or communities, and it has recently been suggested that this division could account for the observed clustering. We demonstrate that group structure in networks can also account for degree correlations. We show using a simple model that we should expect assortative mixing in such networks whenever there is variation in the sizes of the groups and that the predicted level of assortative mixing compares well with that observed in real-world networks.

Subfields: Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ee19e0feffff5b9a97022327abb600310ae9c188

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Citations in the corpus

Influentials, Networks, and Public Opinion Formation (2007, background): “Although this tendency, usually known as “assortativity” (Newman 2002) is a consequence of our model’s assumptions, it is thought to be characteristic of most real social networks (Newman and Park 2003); thus it is not unreasonable to assume that it arises in influence networks as well.”
Statistical analysis of the social network and discussion threads in slashdot (2008, background): “This fact is known as assortative mixing by degree and is present in many social networks [19].”
A Critical Review of Centrality Measures in Social Networks (2010, background): “1 Example of a social network (Newman and Park 2003), we can draw on a variety of existing knowledge from SNA (for an overview cf. Wassermann and Faust 1994).”
Closeness Centrality Extended to Unconnected Graphs: the Harmonic Centrality Index (2009, background): “Those choices may be too naive or restricted: in a near future, we hope to make measurements on assortative (i.e. positive correlation of degrees) networks (Newman & Park, 2003; Xulvi-Brunet & Sokolov, 2004), a common property of social networks.”
Friendship prediction and homophily in social media (2012, methodology): “Figure 5 shows clear assortative trends for several measures for both the aNobii and Flickr datasets, as in other social networks [Newman and Park 2003; McPherson et al. 2001].”
Information network or social network?: the structure of the twitter follow graph (2014, background): “properties observed by others [11, 12].”
Analysis of topological characteristics of huge online social networking services (2007, background): “Far-fetched, we could claim that the top left corner of Figure 1(c) shows negative correlation and the remaining upper cluster displays (although very slight) positive correlation, which is the main characteristic of social networks [17].”
What is Twitter, a social network or a news media? (2010, background): “degree correlation is called assortativity and is known as one of the characteristic features of human social networks [28].”
Impact of human activity patterns on the dynamics of information diffusion. (2007, neutral): “In fact, our data suggest that this is the case; specifically, most of the viral cascades have a tree-like structure while social networks are characterized by the large density of local loops [25].”
Social network analysis of animal behaviour : a promising tool for the study of sociality (2008, background): “There are both striking similarities and differences between diverse networks, and social networks may have some distinct properties of their own (Newman & Park 2003).”