加权迭代节点匹配算法及其在语言网络中的应用

复杂网络间节点匹配在很多领域中均具有重要现实意义。然而，传统的节点匹配算法通常只利用网络的局部拓扑信息，在对拥有高对称性的真实网络作用时往往会失效。为了克服这一缺点，我们近期利用网络拓扑信息和连边权重信息，提出了一种新型的同时来计算不同网络间节点相似度的方法，并在此基础上设计了一种加权迭代节点匹配算法。将该算法在高度拓扑对称仿真网络对和真实中英文语言网络对上分别进行了测试，结果表明加权迭代节点匹配算法在此类网络上优于纯拓扑迭代节点匹配算法。

Weighted Iterative Node Matching Algorithm and Its Application in Language Networks

Node matching between complex networks has practical significance in many areas. However, most of the traditional node matching algorithms which based on the local topological information may lose their efficiencies in many realistic networks, especially in the network with high topological symmetry. In order to overcome this shortage, recently, we proposed a new method to calculate the similarity between two nodes of different networks by utilizing both topological and link-weight information, based on which we designed a weighted iterative node matching algorithm. We test this new algorithm on pairwise artificial networks of high topological symmetry and a pair of real Chinese-English language networks. The results show that the weighted iterative node matching algorithm behaves better than the pure topology based iterative node matching algorithm on this kind of networks.