局部拓扑信息耦合促进网络演化

为了研究局部拓扑信息耦合对网络演化的促进作用，该文提出一种局部拓扑加权方法，用于表征节点间联系的紧密性及拓扑信息的耦合程度，并从演化模型的宏观统计和实际网络数据测试两方面验证了局部拓扑信息耦合促进网络演化的有效性。首先将该加权方法应用于BA模型，提出TwBA模型及局域世界模型TwLW。仿真实验表明，TwBA的度分布随连边数目的增多，迅速从指数分布转变为幂律分布，验证了现实网络加速增长产生幂律分布的现象，并基于此提出一种加速演化的TwBA模型，其在不同的加速率下呈现出幂律分布；而TwLW则展现了从广延指数分布到幂律分布变化的形式。然后将加权方法拓展到链路预测方法，提出3个加权相似性指标。实际网络数据测试表明，该方法能够大幅度地提高基本算法的预测精度，部分甚至高于全局性指标。

Information Coupling of Local Topology Promoting the Network Evolution

To study the effects of information coupling of local topology on the complex network evolution, a new weighted method is proposed based on local topology information, which can measure the closeness of connection and the coupling degree of topology information between nodes. In this paper, to demonstrate the efficiency of the information coupling of local topology, an empirical research is made on characteristic statistics of evolving model and real network data testing of link prediction respectively. Firstly, the weighted method is applied to BA model; TwBA and the local world model TwLW are proposed based on the topology weighted method. Simulation experiments show that the degree distribution of TwBA can be rapidly changed from exponential distribution to power law distribution with the increasing of the connection numbers for new added nodes, which confirmes that the phenomenon of accelerating growth appears widely in the evolution of many real scale-free networks. Then, based on TwBA model, an accelerating growth model A-TwBA is proposed, and the A-TwBA model presents power law distribution for different accelerating growth rates. The degree distribution of TwLW is changed from stretched exponential distribution to power law distribution for different sizes of local world. Finally, the proposed weighted method is applied to link prediction methods (including CN, Salton and RA index), and three weighted indices are proposed. Empirical study shows that the weighted proposed method can significantly improve the prediction accuracy of these basic indices, and some of them are higher than those of the global indices.