复杂网络中拓扑结构和目的地选择策略对智能体动态的影响

Satori Tsuzuki, Daichi Yanagisawa, Eri Itoh, Katsuhiro Nishinari
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引用次数: 0

摘要

我们分析了复杂网络中主体的行为:Barab\ asi-Albert, erdos \ r \ enyi和Watts-Strogatz模型。我们观察了三种场景:(a)智能体在相邻节点中随机选择一个目的地。(b)排除最拥挤的相邻节点作为目的地。(c)始终选择最稀疏相邻节点作为目的地。我们测量了预感率,即单位时间内每个节点中代理数量的变化率,以及节点间代理分布的不平衡性。我们的实证研究表明,当智能体进行完全随机行走时,拓扑网络结构精确地决定了智能体的分布;然而,它们的目的地选择改变了代理的分布。(c)当网络高度高时,使预感率和不平衡率显著高于随机漫步场景。(b)当网络活动低时,预感率增加,不平衡率降低;然而,当活动高时,两者都会增加。这些物理特性随时间呈现周期性波动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effects of topological structure and destination selection strategies on agent dynamics in complex networks
We analyzed agents' behavior in complex networks: Barab\'asi-Albert, Erdos-R\'enyi, and Watts-Strogatz models. We observed three scenarios: (a) Agents randomly selecting a destination among adjacent nodes. (b) Excluding the most congested adjacent node as a destination. (c) Always choosing the sparsest adjacent node as a destination. We measured the hunching rate, that is, the rate of change of agent amounts in each node per unit of time, and the imbalance of agent distribution among nodes. Our empirical study reveals that topological network structure precisely determines agent distribution when agents perform full random walks; however, their destination selections alter the agent distribution. (c) makes hunching and imbalance rates significantly higher than those in random walk scenarios when the network has a high degree. (b) increases the hunching rate while decreasing the imbalance rate when activity is low; however, both increase when activity is high. These physical characteristics exhibited periodic undulations over time.
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