【作者】 张福增； 杨洪勇； 李阿丽；

【Author】 Fuzeng Zhang, Hongyong Yang, Ali Li (School of Computer Science and Technology, LuDong University, Yantai 264025, China)

【机构】 鲁东大学计算科学与技术学院；

【摘要】 一般而言,网络是由内部互连的大量节点组成的集合,其中每一个节点是一个基本单元。在科学研究和人类社会的所有领域都存在网络,比如:许多计算机通过通信媒体连接构成计算机网络、Internet网络上的许多页面通过超链接组成World Wide Web、人与人之间根据某种关系构成社会关系网络、科研文章之间的引用关系构成科学论文的引文网络、科学家之间的合作关系构成科学家合作网络等等。所有这些网络统称为复杂网络。更多还原

【Abstract】 In general, a complex network is a large set of interconnected nodes, in which a node is a fundamental unit with specific contents. The ubiquity of complex networks naturally stimulates the current intensive study of the subject. It has been noticed that there are many inherent complexity issues that lead to tremendous difficulties in understanding various aspects of such complex networks, including the structural complexity, network evolution, connection diversity, dynamical complexity, node diversity, and meta-complication, etc. Complex networks exist in all fields of sciences and humanities, and have been intensively studied over the past decades, among these are computer networks, the World Wide Web, food webs, cellular and metabolic networks, co-authorship and citation networks of scientists, social networks, electrical power grids etc. Traditionally, a network of complex is described by a completely random graph, which is at the opposite end of the spectrum from a completely regular network, the ER model. However, many real-world complex networks are neither completely regular nor completely random, such as the small-world networks introduced by Watts and Strogatz, and the scale-free networks presented by Barabasi and Albert. Network structures often affect the features of the network, for example, the topologies of the society have important impacts on the infection of the disease and the spread of the information, the structures of Internet affect the diffuse fields on the computer virus, the networks of the paper citation relate with the presentation of the new things. Therefore, lots of features of the networks can be realized by studying the network topology. In order to study the development of one science domain and the prevalence of a new idea, it is important to examine the influences of the scientists. Based on the theories of the complex networks and the technologies of the network searches, we study the influences of the different scientists in correlation domain, and expose the role of the scientists for the prevalence of a new techniques and the development of one science. In one research field, many effects in different scientists are used to construct network, such as the cooperation relations (the authors in one paper), the citation relations (whose paper is cited) and the discussions each other (acknowledgements or letter) so on. This network is a directed network with different weights, and the matrix corresponding to networks is an asymmetry matrix with diagonal elements 0. By analyzing the influences between the line nodes, an algorithm is presented to reckon the influences of the scientists. Finally, an example is used to validate this algorithm.更多还原