2013/12/27 by Ari Zitin, Zitin, Ari, Alex Gorowora +11
Environmental Science · Physics and Astronomy · #Complex Network Analysis Techniques #Ecosystem dynamics and resilience #FOS: Computer and information sciences #FOS: Physical sciences #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Statistical Mechanics (cond-mat.stat-mech)
paper · pdf · doi:10.48550/arxiv.1312.7165
openalex publication_date 2013/12/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Networks in nature are often formed within a spatial domain in a dynamical manner, gaining links and nodes as they develop over time. We propose a class of spatially-based growing network models and investigate the relationship between the resulting statistical network properties and the dimension and topology of the space in which the networks are embedded. In particular, we consider models in which nodes are placed one by one in random locations in space, with each such placement followed by configuration relaxation toward uniform node density, and connection of the new node with spatially nearby nodes. We find that such growth processes naturally result in networks with small-world features, including a short characteristic path length and nonzero clustering. These properties do not appear to depend strongly on the topology of the embedding space, but do depend strongly on its dimension; higher-dimensional spaces result in shorter path lengths but less clustering.