Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: of spatial embedding on the networks’ structural characteristics, hover, are rarely taken into account when studying their macroscopic properties. Here,  propose a hierarchy of null models to generate random surrogates from a given spatially embedded network that can preserve global and local statistics associated with the nodes’ embedding in a metric space. Comparing the original network’s and the resulting surrogates’ global characteristics allows to quantify to what extent these characteristics are already predetermined by the spatial embedding of the nodes and links.  apply our framework to various real-world spatial networks and show that the proposed models capture macroscopic properties of the networks under study much better than standard random network models that do not account for the nodes’ spatial embedding. Depending on the actual performance of the proposed null models, the networks are categorized into dierent classes. Since many real-world complex networks are in fact spatial networks, the proposed approach is relevant for disentangling underlying complex system structure from spatial embedding of nodes in many elds, ranging from social systems over infrastructure and neurophysiology to climatology.
['Marc Wiedermann', 'Jonathan F. Donges', 'Jürgen Kurths', 'Reik V. Donner']
I am writing a research paper on a idea Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes
[{"from": "human", "value": "I am writing a research paper on a idea Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: of spatial embedding on the networks\u2019 structural characteristics, hover, are rarely taken into account when studying their macroscopic properties. Here,  propose a hierarchy of null models to generate random surrogates from a given spatially embedded network that can preserve global and local statistics associated with the nodes\u2019 embedding in a metric space. Comparing the original network\u2019s and the resulting surrogates\u2019 global characteristics allows to quantify to what extent these characteristics are already predetermined by the spatial embedding of the nodes and links.  apply our framework to various real-world spatial networks and show that the proposed models capture macroscopic properties of the networks under study much better than standard random network models that do not account for the nodes\u2019 spatial embedding. Depending on the actual performance of the proposed null models, the networks are categorized into dierent classes. Since many real-world complex networks are in fact spatial networks, the proposed approach is relevant for disentangling underlying complex system structure from spatial embedding of nodes in many elds, ranging from social systems over infrastructure and neurophysiology to climatology."}]