智能复杂网络
Intelligent Complex Networks
摘要：
目前的工作解决了使用复杂网络作为人工智能机制的问题。更具体地说，我们考虑的情况下，难题，表示为复杂的网络的不同类型，是由复杂的网络处理引擎的不同结构组装。拼图最初分布在根据不同标准选择的一组节点上，包括度和特征向量中心性。然后，这些片段被重复复制到相邻节点。提供不同大小的缓冲区也进行了研究。确定了几个有趣的结果，包括BA为基础的组装引擎往往提供最快的解决方案。它也被发现，根据特征向量中心的碎片的分布几乎总是导致最好的性能。另一个结果是，使用与各个节点的度成比例的缓冲器大小倾向于提高性能。
英文摘要：
The present work addresses the issue of using complex networks as artificial intelligence mechanisms. More specifically, we consider the situation in which puzzles, represented as complex networks of varied types, are to be assembled by complex network processing engines of diverse structures. The puzzle pieces are initially distributed on a set of nodes chosen according to different criteria, including degree and eigenvector centrality. The pieces are then repeatedly copied to the neighboring nodes. The provision of buffering of different sizes are also investigated. Several interesting results are identified, including the fact that BA-based assembling engines tend to provide the fastest solutions. It is also found that the distribution of pieces according to the eigenvector centrality almost invariably leads to the best performance. Another result is that using the buffer sizes proportional to the degree of the respective nodes tend to improve the performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
