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20032023
most citedMultilayer Networks

3.2k citations · 5.9k across the 54 of their papers we have counts for

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Showing cond-mat.dis-nnShow all

6 papers · 1 filter

cond-mat.dis-nn2015★ 8 cited

Emergence of coexisting percolating clusters in networks

Ali Faqeeh, Sergey Melnik, Pol Colomer-de-Simón +1

It is commonly assumed in percolation theories that at most one percolating cluster can exist in a network. We introduce sausage-like networks (SLNs), an ensemble of synthetic modu…

cond-mat.dis-nn2014★ 2 cited

Analytical approach to the dynamics of facilitated spin models on random networks

Peter G. Fennell, James P. Gleeson, Davide Cellai

Facilitated spin models were introduced some decades ago to mimic systems characterized by a glass transition. Recent developments have shown that a class of facilitated spin model…

cond-mat.dis-nn2013

Singularities in ternary mixtures of k-core percolation

Davide Cellai, James P. Gleeson

Heterogeneous k-core percolation is an extension of a percolation model which has interesting applications to the resilience of networks under random damage. In this model, the not…

cond-mat.dis-nn2012★ 33 cited

Critical phenomena in heterogeneous k-core percolation

Davide Cellai, Aonghus Lawlor, Kenneth A. Dawson +1

-core percolation is a percolation model which gives a notion of network functionality and has many applications in network science. In analysing the resilience of a network und…

cond-mat.dis-nn2011★ 64 cited

Tricritical point in heterogeneous k-core percolation

Davide Cellai, Aonghus Lawlor, Kenneth A. Dawson +1

k-core percolation is an extension of the concept of classical percolation and is particularly relevant to understand the resilience of complex networks under random damage. A new…

cond-mat.dis-nn2010★ 131 cited

The unreasonable effectiveness of tree-based theory for networks with clustering

Sergey Melnik, Adam Hackett, Mason A. Porter +2

We demonstrate that a tree-based theory for various dynamical processes yields extremely accurate results for several networks with high levels of clustering. We find that such a t…