A comparative study of social network models: network evolution models and nodal attribute models

  • Toivonen R
  • Kovanen L
  • Kivelä M
 et al. 
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Abstract

This paper reviews, classifies and compares recent models for social networks
that have mainly been published within the physics-oriented complex networks
literature. The models fall into two categories: those in which the addition of
new links is dependent on the (typically local) network structure (network
evolution models, NEMs), and those in which links are generated based only on
nodal attributes (nodal attribute models, NAMs). An exponential random graph
model (ERGM) with structural dependencies is included for comparison. We fit
models from each of these categories to two empirical acquaintance networks
with respect to basic network properties. We compare higher order structures in
the resulting networks with those in the data, with the aim of determining
which models produce the most realistic network structure with respect to
degree distributions, assortativity, clustering spectra, geodesic path
distributions, and community structure (subgroups with dense internal
connections). We find that the nodal attribute models successfully produce
assortative networks and very clear community structure. However, they generate
unrealistic clustering spectra and peaked degree distributions that do not
match empirical data on large social networks. On the other hand, many of the
network evolution models produce degree distributions and clustering spectra
that agree more closely with data. They also generate assortative networks and
community structure, although often not to the same extent as in the data. The
ERG model turns out to produce the weakest community structure.

Author-supplied keywords

  • Physics and Society

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Authors

  • Riitta Toivonen

  • Lauri Kovanen

  • Mikko Kivelä

  • Jukka-Pekka Onnela

  • Jari Saramäki

  • Kimmo Kaski

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