Spatial Networks

@inproceedings{Barthelemy2014SpatialN,
  title={Spatial Networks},
  author={Marc Barthelemy},
  booktitle={Encyclopedia of Social Network Analysis and Mining},
  year={2014}
}
  • M. Barthelemy
  • Published in
    Encyclopedia of Social…
    2 October 2010
  • Computer Science

Random Spatial Networks: Small Worlds without Clustering, Traveling Waves, and Hop-and-Spread Disease Dynamics

A class of random spatial networks (RSNs) is introduced which generalizes many existing random network models but adds spatial structure, and is used to propose a new generalization of small-world networks, where the average shortest path lengths in the graph are small, but with close spatial proximity of nodes that are neighbors in the network playing the role of large clustering.

From Complex to Spatial Networks

This chapter describes briefly the evolution of these fields and ideas about spatial networks, most of these objects are planar and in the second part of this chapter, the basic definitions and results for planar graphs.

Complex contagions on noisy geometric networks

This work addresses the question of when contagions spread predominantly via the spatial propagation of wavefronts rather than via the appearance of spatially-distant clusters of contagion (as observed for modern epidemics) and finds a deep connection between the fields of dynamical systems and nonlinear dimension reduction.

Spatial structure of complex network and diffusion dynamics

In the recent development of network sciences, spatial constrained networks have become an object of extensive investigation. Spatial constrained networks are embedded in configuration space. Their

Spatial community structure and epidemics

The results indicate that it is important to incorporate spatial information intonull models for community detection, but it is best to incorporate only relevant information into null models, as extraneous information can lower performance.

A Latent Parameter Node-Centric Model for Spatial Networks

A novel model for capturing the interaction between spatial effects and network structure is introduced which attaches a latent variable to each node which represents a node's spatial reach and is inferred from the network structure using a Markov Chain Monte Carlo algorithm.

Temporal Networks

Spatial optimality in power distribution networks

This paper investigates the effect of the wiring cost in the spatial organization of a sample of power distribution networks by means of shuffling the networks in systematic ways and shows that although they share similar topologies, suboptimal networks seem to accumulate more failures.

Modeling spatial social complex networks for dynamical processes

A spatial social complex network (SSCN) model is developed that captures not only essential connectivity features of real-life social networks, including a heavy-tailed degree distribution and high clustering, but also the spatial location of individuals, reproducing Zipf's law for the distribution of city populations as well as other observed hallmarks.
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