2014.

Paper abstract bibtex

Paper abstract bibtex

The organisation of living systems is neither random nor regular, but tends to exhibit complex structure in the form of clustering and modularity. Here, we present a very simple model that generates random networks with spontaneous community structure reminiscent of living systems, particularly those involving social interaction. We extend the well-known random geometric graph model, in which spatially embedded networks are constructed subject to a constraint on edge length, in order to capture two key additional features of organic social networks. First, relationships that span longer distances are more costly to maintain. Conversely, relationships between nodes that share neighbours may be less costly to maintain due to social synergy. The resulting networks have several properties in common with those of organic social networks. We demonstrate that the model generates non-trivial community structure and that, unlike for random geometric graphs, densely connected communities do not simply arise as a consequence of an initial locational advantage.

@unpublished{ eps364826, booktitle = {ALIFE 14: The Fourteenth International Conference on the Synthesis and Simulation of Living Systems}, editor = {Hod Lipson and Hiroki Sayama and John Rieffel and Sebastian Risi and Rene Doursat}, title = {REDS: an energy-constrained spatial social network model}, author = {Alberto Antonioni and Seth Bullock and Marco Tomassini}, publisher = {MIT Press}, year = {2014}, url = {http://eprints.soton.ac.uk/364826/}, abstract = {The organisation of living systems is neither random nor regular, but tends to exhibit complex structure in the form of clustering and modularity. Here, we present a very simple model that generates random networks with spontaneous community structure reminiscent of living systems, particularly those involving social interaction. We extend the well-known random geometric graph model, in which spatially embedded networks are constructed subject to a constraint on edge length, in order to capture two key additional features of organic social networks. First, relationships that span longer distances are more costly to maintain. Conversely, relationships between nodes that share neighbours may be less costly to maintain due to social synergy. The resulting networks have several properties in common with those of organic social networks. We demonstrate that the model generates non-trivial community structure and that, unlike for random geometric graphs, densely connected communities do not simply arise as a consequence of an initial locational advantage.} }

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