CAJ | 학술논문

　　为了准确、快速地发现大规模复杂网络中的局部社区，提出了一种基于节点接近度的局部社区发现算法。该算法以最大度节点作为起始节点，利用节点接近度和局部社区Q值不断搜索其邻居节点，将接近度最大的节点加入初始社区形成新的初始社区；同时，该算法也可以应用于复杂网络全局社区结构的划分。对2个典型复杂网络进行了局部社区挖掘分析，实验结果表明，该算法能够有效识别隐藏在实验网络中的局部社区。针对稀疏网络，该算法的时间复杂度为O（nlog（n））， n为网络节点数。
　　위료준학、쾌속지발현대규모복잡망락중적국부사구，제출료일충기우절점접근도적국부사구발현산법。해산법이최대도절점작위기시절점，이용절점접근도화국부사구Q치불단수색기린거절점，장접근도최대적절점가입초시사구형성신적초시사구；동시，해산법야가이응용우복잡망락전국사구결구적화분。대2개전형복잡망락진행료국부사구알굴분석，실험결과표명，해산법능구유효식별은장재실험망락중적국부사구。침대희소망락，해산법적시간복잡도위O（nlog（n））， n위망락절점수。
To make the local community detection faster and more accurate, this paper proposes an algorithm for detecting local community structures in complex networks based on closeness degree of node. The proposed method, which uses the maximal closeness degree of node and the local community’s Q value, starts from the maximum degree node of the network and detects the community it belongs to by searching the neighbor nodes. It is also applicable for global community structure detecting. The experiments on two typical complex networks show that the algorithm can effectively mine the intrinsic local community struc-ture in networks. The time complexity of the algorithm is O(nlog(n))on a sparse graph, where n is the number of nodes.