Adami Vadood, Ebadi Zahra, Nattagh-Najafi Morteza
Department of Physics, University of Mohaghegh Ardabili, P.O. Box 179, Ardabil, Iran.
Sci Rep. 2024 Jul 23;14(1):16994. doi: 10.1038/s41598-024-67896-9.
In this paper we introduce a new type of preferential attachment network, the growth of which is based on the eigenvector centrality. In this network, the agents attach most probably to the nodes with larger eigenvector centrality which represents that the agent has stronger connections. A new network is presented, namely a dandelion network, which shares some properties of star-like structure and also a hierarchical network. We show that this network, having hub-and-spoke topology is not generally scale free, and shows essential differences with respect to the Barabási-Albert preferential attachment model. Most importantly, there is a super hub agent in the system (identified by a pronounced peak in the spectrum), and the other agents are classified in terms of the distance to this super-hub. We explore a plenty of statistical centralities like the nodes degree, the betweenness and the eigenvector centrality, along with various measures of structure like the community and hierarchical structures, and the clustering coefficient. Global measures like the shortest path statistics and the self-similarity are also examined.
在本文中，我们介绍了一种新型的偏好依附网络，其增长基于特征向量中心性。在这个网络中，主体最有可能依附于具有较大特征向量中心性的节点，这表示该主体具有更强的连接。我们提出了一种新的网络，即蒲公英网络，它具有一些星状结构的特性，同时也是一种层次网络。我们表明，这种具有中心辐射拓扑结构的网络通常不是无标度的，并且与巴拉巴西 - 阿尔伯特偏好依附模型存在本质区别。最重要的是，系统中存在一个超级中心主体（由频谱中的明显峰值确定），其他主体则根据与这个超级中心的距离进行分类。我们探索了大量的统计中心性指标，如节点度、介数和特征向量中心性，以及各种结构度量，如社区和层次结构以及聚类系数。还研究了最短路径统计和自相似性等全局度量。
