摘要翻译:
研究了规则节点和随机集线器组成的2-D通信网络中的梯度消息传递机制的效率。网络上的每个集线器都被分配一些随机选择的容量,容量较低的集线器被连接到容量最大的集线器。单个消息在这个网格上的平均旅行时间,作为集线器密度的函数,显示出q指数行为。在高轮毂密度下,这种分布可以用幂律很好地拟合。我们还研究了当大量消息在随机位置同时产生,并在网络上向指定的目的地传播时,网络的松弛行为。对于这种情况,在没有梯度机制的情况下,由于传输陷阱的形成,网络会表现出拥塞效应。我们表明,如果高介数中心度的枢纽通过梯度机制连接起来,可以实现有效的解充血。梯度机制比其他减充血方案更不容易形成陷阱。我们还研究了运输陷阱的空间构型,并提出了消除它们的最小策略。
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英文标题:
《The Gradient Mechanism in a Communication Network》
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作者:
Satyam Mukherjee and Neelima Gupte
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最新提交年份:
2007
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分类信息:
一级分类:Physics 物理学
二级分类:Physics and Society 物理学与社会
分类描述:Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体(人类或其他)的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统(如能源网、运输网络)的物理和工程。
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一级分类:Physics 物理学
二级分类:Statistical Mechanics 统计力学
分类描述:Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变,热力学,场论,非平衡现象,重整化群和标度,可积模型,湍流
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一级分类:Physics 物理学
二级分类:Adaptation and Self-Organizing Systems 自适应和自组织系统
分类描述:Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应,自组织系统,统计物理,波动系统,随机过程,相互作用粒子系统,机器学习
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英文摘要:
We study the efficiency of the gradient mechanism of message transfer in a $2-d$ communication network of regular nodes and randomly distributed hubs. Each hub on the network is assigned some randomly chosen capacity and hubs with lower capacities are connected to the hubs with maximum capacity. The average travel time of single messages traveling on this lattice, plotted as a function of hub density, shows q-exponential behavior. At high hub densities, this distribution can be fitted well by a power law. We also study the relaxation behavior of the network when a large number of messages are created simultaneously at random locations, and travel on the network towards their designated destinations. For this situation, in the absence of the gradient mechanism, the network can show congestion effects due to the formation of transport traps. We show that if hubs of high betweenness centrality are connected by the gradient mechanism, efficient decongestion can be achieved. The gradient mechanism is less prone to the formation of traps than other decongestion schemes. We also study the spatial configurations of transport traps, and propose minimal strategies for their elimination.
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PDF链接:
https://arxiv.org/pdf/709.4371