结合局部相似性的模块密度法探测网络社区

The New Detection Method to Network Community with the Module Degree in Combination with Local Similarity

多数基于极值优化的社区发现算法对初始划分很敏感,并且因为计算过程中极值产生震荡而难于达到最优.提出利用顶点的度选取核心点和局部相似度进行核心点划分并采用启发式方法将剩余节点加入划分,在改进上述算法缺点的基础上,利用实际社会网络数据集进行实验证明了方法的有效性.

Most of the community discovery algorithms based on extreme values optimization are sensitive of the division of the initial class, and it is difficult to achieve optimal because of vibra- tion in extreme value on the process of computing. Proposed the method of selecting core vertexes based on vertex degree, core vertexes are divided to two classed based local similarity and the rest of vertexes are jointed in the two classes according to heuristic optimization methods. This method over- comes the disadvantage mentioned above, experiments are conducted to prove the effectiveness of the method in real social network data sets.