Open AccessA complex network evolution model for network growth promoted by information transmission
Author(s) -
Shuxin Liu,
Xinsheng Ji,
Caixia Liu,
Guo Hong
Publication year - 2014
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.63.158902
Subject(s) - average path length , degree distribution , computer science , node (physics) , complex network , transmission (telecommunications) , clustering coefficient , path (computing) , network model , cluster analysis , preferential attachment , scale free network , topology (electrical circuits) , network simulation , theoretical computer science , computer network , data mining , telecommunications , mathematics , physics , shortest path problem , artificial intelligence , world wide web , graph , quantum mechanics , combinatorics
In many real complex networks, information transmission occurs all the time. To study the effects of information transmission on the complex network evolution, we propose a new model for network growth promoted by the information transmission. The model includes three major steps: (i) New links attached to the nodes on the information transmission path, whose source point is chosen preferentially; (ii) the first link of the new node attached to the nodes in the local-world; (iii) other links of the new node attached to the nodes on the information transmission path, whose source point is the new node. The process of information transmission is simulated by self-avoiding random walk, and by considering the local information including its degree and distance; selective connection is established between the nodes on the information transmission path. Theoretical analysis and numerical simulation results show that the proposed model can not only reproduce small-world and scale-free network characteristics, but also indicate that shift power-law distribution and truncated power law function may form for different parameters which have some non-power-law features, such as exponential cutoff, and saturation for small variables. Moreover, in our model, the clustering coefficient is tunable without changing the degree distribution, and the model can also construct a network with assortative or disassortative mixed pattern.