REVIEW 2 cited by
Correlated couplings and robustness of coupled networks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Most real-world complex systems can be modelled by coupled networks with multiple layers. How and to what extent the pattern of couplings between network layers may influence the interlaced structure and function of coupled networks are not clearly understood. Here we study the impact of correlated inter-layer couplings on the network robustness of coupled networks using percolation concept. We found that the positive correlated inter-layer coupling enhaces network robustness in the sense that it lowers the percolation threshold of the interlaced network than the negative correlated coupling case. At the same time, however, positive inter-layer correlation leads to smaller giant component size in the well-connected region, suggesting potential disadvantage for network connectivity, as demonstrated also with some real-world coupled network structures.
Forward citations
Cited by 2 Pith papers
-
Misinformation spreading on correlated multiplex networks
A heterogeneous edge-based compartmental theory predicts the outbreak threshold and final size of misinformation spreading on correlated two-layer networks, with good agreement to simulations.
-
Class-Aware Reinforcement Learning for Counterfactual Explanation Generation
Including the target model's predicted class in the RL state representation increases counterfactual validity and speeds up training compared with a class-blind state.
Discussion (0). Continue with ORCID to comment.