# Why social networks are different from other types of networks.

@article{Newman2003WhySN, title={Why social networks are different from other types of networks.}, author={Mark E. J. Newman and Juyong Park}, journal={Physical review. E, Statistical, nonlinear, and soft matter physics}, year={2003}, volume={68 3 Pt 2}, pages={ 036122 } }

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…

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## 40 References

### Assortative mixing in networks.

- 2002

Computer Science

Physical review letters

This work proposes a model of an assortatively mixed network and finds that networks percolate more easily if they are assortative and that they are also more robust to vertex removal.

### Structure of growing social networks.

- 2001

Economics

Physical review. E, Statistical, nonlinear, and soft matter physics

Using computer simulations, it is found that models that incorporate all of these features reproduce many of the features of real social networks, including high levels of clustering or network transitivity and strong community structure in which individuals have more links to others within their community than to individuals from other communities.

### Mixing patterns in networks.

- 2003

Computer Science

Physical review. E, Statistical, nonlinear, and soft matter physics

This work proposes a number of measures of assortative mixing appropriate to the various mixing types, and applies them to a variety of real-world networks, showing that assortsative mixing is a pervasive phenomenon found in many networks.

### Community structure in social and biological networks

- 2002

Computer Science

Proceedings of the National Academy of Sciences of the United States of America

This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.

### Self-similar community structure in a network of human interactions.

- 2003

Computer Science

Physical review. E, Statistical, nonlinear, and soft matter physics

The results reveal the self-organization of the network into a state where the distribution of community sizes is self-similar, suggesting that a universal mechanism, responsible for emergence of scaling in other self-organized complex systems, as, for instance, river networks, could also be the underlying driving force in the formation and evolution of social networks.

### Hierarchical organization in complex networks.

- 2003

Computer Science

Physical review. E, Statistical, nonlinear, and soft matter physics

It is found that several real networks, such as the Worldwideweb, actor network, the Internet at the domain level, and the semantic web obey this scaling law, indicating that hierarchy is a fundamental characteristic of many complex systems.

### The Structure and Function of Complex Networks

- 2003

Computer Science

SIAM Rev.

Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

### Highly clustered scale-free networks.

- 2002

Computer Science

Physical review. E, Statistical, nonlinear, and soft matter physics

The model shows stylized features of real-world networks: power-law distribution of degree, linear preferential attachment of new links, and a negative correlation between the age of a node and its link attachment rate.

### Evolving networks with distance preferences.

- 2002

Computer Science

Physical review. E, Statistical, nonlinear, and soft matter physics

Simulation results for network parameters like the first eigenvalue of the graph Laplacian, clustering coefficients, average distances, and degree distributions for different distance preferences and compare them with the parameter values for random and scale-free networks find that for the shortest distance rule, a power-law degree distribution is obtained.

### Origin of degree correlations in the Internet and other networks.

- 2003

Geology

Physical review. E, Statistical, nonlinear, and soft matter physics

The results confirm that the conjectured mechanism does indeed give rise to correlations of the kind seen in the Internet, although only a part of the measured correlation can be accounted for in this way.