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Characterizing the Structural Complexity of RealWorld Complex Networks
"... Abstract. Although recent research has shown that the complexity of a network depends on its structural organization, which is linked to the functional constraints the network must satisfy, there is still no systematic study on how to distinguish topological structure and measure the corresponding s ..."
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Abstract. Although recent research has shown that the complexity of a network depends on its structural organization, which is linked to the functional constraints the network must satisfy, there is still no systematic study on how to distinguish topological structure and measure the corresponding structural complexity of complex networks. In this paper, we propose the first consistent framework for distinguishing and measuring the structural complexity of realworld complex networks. In terms of the smallest d of the dK model with highorder constraints necessary for fitting real networks, we can classify realworld networks into different structural complexity levels. We demonstrate the approach by measuring and classifying a variety of realworld networks, including biological and technological networks, smallworld and nonsmallworld networks, and spatial and nonspatial networks.
Tradeoffs in HighFidelity Modeling of Spatial Complex Systems
"... Research in complex networks has focused on the use of random graph generators as models for the topology of complex systems. Although a range of generators has been proposed, the standard generators cannot capture the topology of many systems with high fidelity. We argue that parametertuning is ne ..."
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Research in complex networks has focused on the use of random graph generators as models for the topology of complex systems. Although a range of generators has been proposed, the standard generators cannot capture the topology of many systems with high fidelity. We argue that parametertuning is necessary to enable graph generators to create highfidelity models for complex systems. Generating highfidelity models thus entails tradeoffs between: (1) complexity of a model and the number of parameters, and (2) the model’s explanatory power and associated generality. We illustrate the domainanalysis necessary for selecting the best model, together with its associated parameters for a variety of complex systems with spatial constraints. 1
1Analysing the Scaling of Connectivity in Neuromorphic Hardware and in Models of Neural Networks
"... © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to s ..."
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© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Adaptive Fractallike Network Structure for Efcient Search of Targets at Unknown Positions and for Cooperative Routing
"... AbstractFrom viewpoints of complex network science and biological foraging for communication networks, we propose a system model of scalable selforganized geographical networks, in which the proper positions of nodes and the network topology are simultaneously determined according to population. T ..."
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AbstractFrom viewpoints of complex network science and biological foraging for communication networks, we propose a system model of scalable selforganized geographical networks, in which the proper positions of nodes and the network topology are simultaneously determined according to population. The fractallike network structure is constructed by iterative divisions of rectangles for load balancing across nodes, in order to adapt to territory changes. In numerical simulations, we show that, for searching targets concentrated around high population areas, the naturally embedded fractallike structure by population has higher efciency than the conventionally optimal strategy on a square lattice. The adaptation of network structure to the spatial distribution of realistic communication requests gives such a high performance.