Niizato Takayuki, Gunji Yukio-Pegio
Faculty of Engineering, Information and Systems, Tsukuba University, Tsukuba, Ibaraki, Japan.
School of Fundamental Science and Engineering, Waseda University, Tokyo, Japan.
PLoS One. 2015 May 18;10(5):e0127284. doi: 10.1371/journal.pone.0127284. eCollection 2015.
In real networks, the resources that make up the nodes and edges are finite. This constraint poses a serious problem for network modeling, namely, the compatibility between robustness and efficiency. However, these concepts are generally in conflict with each other. In this study, we propose a new fitness-driven network model for finite resources. In our model, each individual has its own fitness, which it tries to increase. The main assumption in fitness-driven networks is that incomplete estimation of fitness results in a dynamical growing network. By taking into account these internal dynamics, nodes and edges emerge as a result of exchanges between finite resources. We show that our network model exhibits exponential distributions in the in- and out-degree distributions and a power law distribution of edge weights. Furthermore, our network model resolves the trade-off relationship between robustness and efficiency. Our result suggests that growing and anti-growing networks are the result of resolving the trade-off problem itself.
在真实网络中，构成节点和边的资源是有限的。这种约束给网络建模带来了一个严重问题，即鲁棒性与效率之间的兼容性。然而，这些概念通常相互冲突。在本研究中，我们提出了一种用于有限资源的新的适应度驱动网络模型。在我们的模型中，每个个体都有其自身试图提高的适应度。适应度驱动网络的主要假设是适应度的不完整估计导致动态增长的网络。通过考虑这些内部动态，节点和边作为有限资源之间交换的结果而出现。我们表明，我们的网络模型在入度和出度分布中呈现指数分布，在边权重上呈现幂律分布。此外，我们的网络模型解决了鲁棒性与效率之间的权衡关系。我们的结果表明，增长和反增长网络是解决权衡问题本身的结果。
