复杂通信网络的地理位置聚集性社团发现和可视化

针对以通信网络为代表的一类复杂网络地理位置信息的聚集性与网络结构一定程度上的正相关性，探讨了将地理位置信息带入特定的复杂网络的社团发现和可视化任务中，改进传统的标号传播和力导引算法，提前进行网络的地理位置聚类分析，并对标号传播的和力导引的迭代过程引入基于地理位置的限制性条件，避免无意义的振荡。实验证明，提出的方法既可以加快社团发现和可视化算法的收敛速度，也可以通过地理位置对社团分布的影响提高快速社团发现算法的性能。针对存在地理位置聚集性的复杂网络数据，该方法无论在收敛时间还是社团发现结果（Q值）上都有较大提升。

Complex communication network geolocation based community detection and visualization

The geolocation is believed to have certain positive correlation with network structure in the communication networks,shopping network and other complex networks.The geolocation information is introduced into the task of complex network group detecting and visualization to improve the traditional label propagation algorithm and force-directed graph drawing algorithm. By performing the geolocation based clustering in advance,and then adding the geolocation based restriction in the iterative process,meaningless oscillations can be greatly minimized. The experiment proves that this scheme can speed up the discovery of community and the convergence speed of the algorithm can also be added to the influence of geographical location on the distribution of the community,and the performance of the fast community discovery algorithm can be improved both in convergence time and community discovery(Q value).