社交网络数据动态聚类调度算法实现

社交网络数据的庞大规模与复杂结构增加了目标数据获取难度,为此,提出一种社交网络数据动态聚类调度算法,根据节点密度值计算节点距离值,得到Z-score标准化后的密度-距离值,将标签分配给密度-距离较大值的对应节点,完成中心点识别,构建标签种子区域,按照降序密度-距离值更新标签,优先把标签分配至重要节点,实现社交网络数据的...

Implementation of Dynamic Clustering Scheduling Algorithm for Social Network Data

The huge scale and complex structure of social network data make it difficult to obtain the target data.In this regard,a dynamic clustering scheduling algorithm for social network data is proposed in this work.The node distance value was calculated by the node density value,the density distance value after Z-score standardization was obtained.The label was assigned to the corresponding node with larger density distance value to complete the center point recognition.The label seed area was established.Based on the descending density distance value,the label was updated.Important nodes got labels first,the dynamic clustering of social network data was realized.The distribution of data passband characteristics was obtained via the extraction of dynamic migration load characteristics.Based on the output coupling eigenvector and iterative function equation,data balancing scheduling was completed.Accuracy,standard mutual information,modularity and Rand index were utilized to evaluate the effectiveness of the algorithm.The simulation results show that the algorithm has excellent dynamic clustering advantages,balanced data transmission and high efficiency.