内生星型网络拓扑中拥塞信息提供对优化代理动力学的影响
Effect of congestion avoidance due to congestion information provision on optimizing agent dynamics on an endogenous star network topology
摘要：
对网络拓扑结构进行基础研究的重要性已得到广泛认可。本研究的目的是阐明拥塞避免代理的拥塞信息优化网络拓扑结构中的流量的效果。我们研究了随机交通网络中的一个星型拓扑结构与一个中心节点连接到孤立的次级节点具有不同的偏好。在中心节点处的每个代理通过参考基于次级节点的拥塞率的拒绝偏好来选择次级节点。我们研究了两种情况：1）每个代理可以重复访问中心节点和辅助节点。2)每个代理只能访问每个辅助节点一次。对于1），我们研究了代理分布在静止状态下的均匀性，对于2），我们测量了所有代理访问所有节点的旅行时间。当代理重复访问中心和其他节点，代理分布的均匀性已被发现，显示出三种类型的非线性依赖于节点的增加。我们发现，多变量统计描述这些特征的依赖关系，这表明通过避免拥塞和相互引用拥塞信息所引起的协方差的网络使用的均衡之间的平衡确定的均匀性。我们发现，回避线性化的旅行时间，这与节点的数量呈指数级增长，尽管程度的参考拥塞信息。因此，我们成功地描述了优化效果的回避的集体动力学的代理星型拓扑结构。我们的发现在网络科学的许多领域都很有用。
英文摘要：
The importance of fundamental research on network topologies is widely acknowledged. This study aims to elucidate the effect of congestion avoidance of agents given congestion information on optimizing traffic in a network topology. We investigated stochastic traffic networks in a star topology with a central node connected to isolated secondary nodes with different preferences. Each agent at the central node selects a secondary node by referring to the declining preferences based on the congestion rate of the secondary nodes. We examined two scenarios: 1) Each agent can repeatedly visit the central and secondary nodes. 2) Each agent can access each secondary node only once. For 1), we investigated the uniformity of the agent distribution in a stationary state, and for 2), we measured the travel time for all agents visiting all nodes. When agents repeatedly visit central and other nodes, the uniformity of agent distribution has been found to show three types of nonlinear dependence on the increase in nodes. We found that multivariate statistics describe these characteristic dependences well, suggesting that the balance between the equalization of network usage by avoiding congestion and the covariance caused by mutual referral to congestion information determines the uniformity. We discovered that congestion-avoidance linearizes the travel time, which increases exponentially with the number of nodes, notwithstanding the degree of reference to the congestion information. Consequently, we successfully described the optimization effect of congestion-avoidance on the collective dynamics of agents in star topologies. Our findings are useful in many areas of network science.
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