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researcher

S. Johnson

4 papers hereh-index 5118 citations11 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • physics.soc-ph2
  • cond-mat.dis-nn1
  • nlin.AO1
same name
  • S. Johnson — 53 papers, h 23
  • S. Johnson — 34 papers, h 21
  • S. Johnson — 30 papers, h 28
  • S. Johnson — 25 papers, h 22
  • S. Johnson — 13 papers, h 9
  • S. Johnson — 12 papers, h 9

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172022
most citedConcurrence of form and function in developing networks and its role in synaptic pruning

22 citations · 41 across the 4 of their papers we have counts for

collaborators

4 papers

physics.soc-ph2022

Influence and Influenceability: Global Directionality in Directed Complex Networks

Niall Rodgers, Peter Tino, Samuel Johnson

Knowing which nodes are influential in a complex network and whether the network can be influenced by a small subset of nodes is a key part of network analysis. However, many tradi…

cond-mat.dis-nn2022★ 17 cited

Strong Connectivity in Real Directed Networks

Niall Rodgers, Peter Tino, Samuel Johnson

In many real, directed networks, the strongly connected component of nodes which are mutually reachable is very small. This does not fit with current theory, based on random graphs…

physics.soc-ph2018★ 2 cited

Growth strategy determines network performance

Ana P. Millán, J. J. Torres, S. Johnson +1

The interplay between structure and function is crucial in determining some emerging properties of many natural systems. Here we use an adaptive neural network model inspired in ob…

nlin.AO2017★ 22 cited

Concurrence of form and function in developing networks and its role in synaptic pruning

Ana P. Millán, J. J. Torres, S. Johnson +1

A fundamental question in neuroscience is how structure and function of neural systems are related. We study this interplay by combining a familiar auto-associative neural network…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.