CAJ | 학술논문

在复杂网络中发现和刻画社区结构是近年来复杂网络研究的重点方向之一。提出了一种社区挖掘的新思路，即根据力导向模型的原理，通过计算社区与节点之间的作用力来决定节点的社区归属。根据该思路设计了基于力导向模型的算法框架FDCD（Force-directed Community Detect），并利用FR模型、KK模型、LL模型和Q模型进行了验证。实验表明，基于FDCD算法框架的多种不同算法不仅能较好地识别社区结构，而且基于LL模型的FDCD算法达到了线性计算复杂度，能适用于大规模网络的社区挖掘。
재복잡망락중발현화각화사구결구시근년래복잡망락연구적중점방향지일。제출료일충사구알굴적신사로，즉근거력도향모형적원리，통과계산사구여절점지간적작용력래결정절점적사구귀속。근거해사로설계료기우력도향모형적산법광가FDCD（Force-directed Community Detect），병이용FR모형、KK모형、LL모형화Q모형진행료험증。실험표명，기우FDCD산법광가적다충불동산법불부능교호지식별사구결구，이차기우LL모형적FDCD산법체도료선성계산복잡도，능괄용우대규모망락적사구알굴。
Aimed at the problem of detecting and characterizing community structure is one of the outstanding issues in the study of complex network,a new community detect algorithm based on Force-directed model was proposed,which categorizes the point to community decided by the force between them.An algorithm named Force-Directed Community Detect,FDCD,and an implementing algorithm using four different Force-directed Models were designed.The experiments show that the algorithms can find community in real social network with high Q Modularity,and each efficiency of algorithm based on LL model reaches the complexity degree of linear computation,which proves fit for the community detection in large network.