Assortative mixing in spatially-extended networks
Abstract We focus on spatially-extended networks during their transition from short-range connectivities to a scale-free structure expressed by heavy-tailed degree-distribution. In particular, a model is introduced for the generation of such graphs, which combines spatial growth and preferential att...
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| Main Authors: | , , , , , , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2018
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| Subjects: | |
| Online Access: | https://doaj.org/article/fe8e6fcbcbec41e39a30bed642d73fd8 |
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| Summary: | Abstract We focus on spatially-extended networks during their transition from short-range connectivities to a scale-free structure expressed by heavy-tailed degree-distribution. In particular, a model is introduced for the generation of such graphs, which combines spatial growth and preferential attachment. In this model the transition to heterogeneous structures is always accompanied by a change in the graph’s degree-degree correlation properties: while high assortativity levels characterize the dominance of short distance couplings, long-range connectivity structures are associated with small amounts of disassortativity. Our results allow to infer that a disassortative mixing is essential for establishing long-range links. We discuss also how our findings are consistent with recent experimental studies of 2-dimensional neuronal cultures. |
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