<P> Randomly distributed networks: Exponential random graph models of social networks became state - of - the - art methods of social network analysis in the 1980s . This framework has the capacity to represent social - structural effects commonly observed in many human social networks, including general degree - based structural effects commonly observed in many human social networks as well as reciprocity and transitivity, and at the node - level, homophily and attribute - based activity and popularity effects, as derived from explicit hypotheses about dependencies among network ties . Parameters are given in terms of the prevalence of small subgraph configurations in the network and can be interpreted as describing the combinations of local social processes from which a given network emerges . These probability models for networks on a given set of actors allow generalization beyond the restrictive dyadic independence assumption of micro-networks, allowing models to be built from theoretical structural foundations of social behavior . </P> <P> Scale - free networks: A scale - free network is a network whose degree distribution follows a power law, at least asymptotically . In network theory a scale - free ideal network is a random network with a degree distribution that unravels the size distribution of social groups . Specific characteristics of scale - free networks vary with the theories and analytical tools used to create them, however, in general, scale - free networks have some common characteristics . One notable characteristic in a scale - free network is the relative commonness of vertices with a degree that greatly exceeds the average . The highest - degree nodes are often called "hubs", and may serve specific purposes in their networks, although this depends greatly on the social context . Another general characteristic of scale - free networks is the clustering coefficient distribution, which decreases as the node degree increases . This distribution also follows a power law . The Barabási model of network evolution shown above is an example of a scale - free network . </P> <P> Rather than tracing interpersonal interactions, macro-level analyses generally trace the outcomes of interactions, such as economic or other resource transfer interactions over a large population . </P> <P> Large - scale networks: Large - scale network is a term somewhat synonymous with "macro-level" as used, primarily, in social and behavioral sciences, in economics . Originally, the term was used extensively in the computer sciences (see large - scale network mapping). </P>

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