为了更好地研究复杂网络结构，采用随机游走算法实现复杂网络的生成。该算法采用扩散策略实现网络生成，根据活跃标签标注下一次抽取与前一次抽取的联系，除第一个节点之外的所有网络节点的加入均由网络节点属性决定，以最大转移概率为准则来实现下一个节点的选择，有效避免局部陷入与遍历不全的不足。实验证明，相比于传统的复杂网络生成方法，该算法能更好地反映复杂网络的原始网络结构，网络连通性好，度分布和聚类系数与原始网络更相似，网络层次性更强。%In order to better study the complex network structure,we use random walk algorithm to realise the generation of complex network.The algorithm adopts diffusion strategy to achieve network generation,marks the connection between the next-time extraction and the previous one according to active labels.All the joining of the network nodes are determined by network node attributes except the first one, and the selection of next node is implemented by taking the maximum probability as criterion,thus effectively prevents from the insufficiencies of falling into local optimum and incomplete traversal.Experiment proves that compared with traditional complex network generation method, the algorithm proposed in this paper can better reflect the original network structure of complex networks.It has good network connectivity, the degree distribution and clustering coefficient are more similar to the original network,and the network hierarchy is higher as well.
展开▼
