A Distributed Algorithm Based on Local Centrality for Dynamic Social Network Re-construction in Multi-Agent Systems

Bing Xie, Xingju Lu
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Abstract

In this paper, we focus on a self-adaptive network reconstruction problem in a distributed multi-agent system. In this system, the relationship among agents forms a complex network, in which each agent plays two roles, task execution and communication. As agents move, the topology of the relationship changes, which could reduce the system's performance. To guarantee the performance, we design a local centrality for evaluating the vitality of nodes in the network, and propose a distributed re-construction algorithm (DRA) based on the local centrality for re-constructing the dynamic network. To test the efficiency of DRA, we integrate DRA into our former proposed dynamic task allocation algorithm to form a new dynamic task allocation algorithm. The experiment shows that our new improved algorithm performs more efficiently when compared to other dynamic task allocation algorithms. The results illustrate the efficiency of reconstructed dynamic complex networks, which also indicates that the local centrality preforms efficient. Finally, we replace the local centrality with degree centrality and LocalRank in DRA and execute a series of experiments for performance evaluation. The results demonstrate the high efficiency of LC.
基于局部中心性的分布式多智能体系统动态社会网络重构算法
本文研究了分布式多智能体系统中的自适应网络重构问题。在这个系统中,智能体之间的关系形成了一个复杂的网络,每个智能体扮演着任务执行和通信两个角色。随着代理的移动,关系的拓扑结构会发生变化,这可能会降低系统的性能。为了保证性能,我们设计了一个局部中心性来评估网络中节点的活力,并提出了一种基于局部中心性的分布式重构算法(DRA)来重构动态网络。为了验证DRA算法的有效性,我们将DRA算法集成到之前提出的动态任务分配算法中,形成一个新的动态任务分配算法。实验表明,与其他动态任务分配算法相比,改进后的算法具有更高的效率。结果表明,重构的动态复杂网络是有效的,局部中心性是有效的。最后,我们将DRA中的局部中心性替换为度中心性和LocalRank,并进行了一系列的性能评估实验。结果表明了LC的高效率。
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