# Network nestedness as generalized core-periphery structures

@article{Lee2016NetworkNA, title={Network nestedness as generalized core-periphery structures}, author={Sang Hoon Lee}, journal={Physical review. E}, year={2016}, volume={93 2}, pages={ 022306 } }

The concept of nestedness, in particular for ecological and economical networks, has been introduced as a structural characteristic of real interacting systems. We suggest that the nestedness is in fact another way to express a mesoscale network property called the core-periphery structure. With real ecological mutualistic networks and synthetic model networks, we reveal the strong correlation between the nestedness and core-periphery-ness (likeness to the core-periphery structure), by defining…

## 15 Citations

Core-periphery structure in mutualistic networks: an epitaph for nestedness?

- Environmental SciencebioRxiv
- 2020

The concept of core-periphery may be more relevant for studies on mutualistic networks than the concept of nestedness, as ecologists are usually not interested in exploring in detail the ordered subsets that characterize nestedness but instead use nestedness to describe a topology with a core of densely linked generalists surrounded by a sparsely linked periphery of specialists.

Significance of the Nested Structure in Multiplex World Trade Networks

- Computer ScienceComplex.
- 2020

It is argued that nestedness is still a unique feature of the network because of the randomizations of two null models remark that most nested structures are not determined by network density and degree sequences but closely related to them.

Nestedness in complex networks: Observation, emergence, and implications

- Environmental SciencePhysics Reports
- 2019

Macro- and mesoscale pattern interdependencies in complex networks

- Environmental ScienceJournal of the Royal Society Interface
- 2019

It is shown experimentally and analytically that nestedness imposes bounds to modularity, with exact analytical results in idealized scenarios, and it is shown that nesteds and modularity are interdependent.

Core-Periphery Structure in Networks (Revisited)

- Computer ScienceSIAM Rev.
- 2017

This paper develops a new method to investigate the meso-scale feature known as core-periphery structure, which entails identifying densely connected core nodes and sparsely connected peripheral nodes in a network.

Nestedness Maximization in Complex Networks through the Fitness-Complexity Algorithm

- Computer ScienceEntropy
- 2018

It is shown that the fitness-complexity algorithm is highly effective in the nestedness maximization task and generates matrices that are more nested than the optimal ones by BINMATNEST for 61.27% of the analyzed mutualistic networks.

Bridging nestedness and economic complexity in multilayer world trade networks

- Economics
- 2020

Understanding the complexity of international trading is critical for a variety of issues ranging from quantifying the competitiveness of individual nations to forecasting the collective evolution of…

Core-periphery dynamics in a plant-pollinator network.

- Environmental ScienceThe Journal of animal ecology
- 2020

This work used six years of observation data and a novel statistical method (dynamic stochastic block models) to assess how network structure and species' structural position within the network change throughout subseasons of the flowering season and across years in a quantitative plant-pollinator network from a dryland ecosystem in Argentina.

Core–periphery dynamics in a plant–pollinator network

- Environmental SciencebioRxiv
- 2019

This work used six years of observation data and a novel statistical method (dynamic stochastic block models) to assess how network structure and species’ structural position within the network change across time in a quantitative plant–pollinator network from a dryland ecosystem in Argentina.

Detection of core–periphery structure in networks using spectral methods and geodesic paths

- Computer ScienceEuropean Journal of Applied Mathematics
- 2016

Several novel and computationally efficient methods for detecting “core–periphery structure” in networks, which aggregates information from many geodesic paths in a network and yields a score for each vertex that reflects the likelihood that that vertex is a core vertex.

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