CAJ | 학술논문

发现网络中的社团结构有助于更好地理解网络结构和分析网络属性。通过定义边的聚类系数和基于局部信息的方法，提出了一种寻找复杂网络中社团结构的算法。该算法首先在网络的剩余节点中寻找度最大的节点，然后利用该节点的局部信息、边的聚类系数和凝聚的思想，得到复杂网络的社团结构。在两个典型网络上的测试结果表明了该方法的可行性。
발현망락중적사단결구유조우경호지리해망락결구화분석망락속성。통과정의변적취류계수화기우국부신식적방법，제출료일충심조복잡망락중사단결구적산법。해산법수선재망락적잉여절점중심조도최대적절점，연후이용해절점적국부신식、변적취류계수화응취적사상，득도복잡망락적사단결구。재량개전형망락상적측시결과표명료해방법적가행성。
Detecting community structure in complex network contributes to understand the network structures and analyze the network properties better. Based on the local information, this article proposes an algorithm for discovering the communities in complex network by introducing the definition of the edge cluster coefficient. To obtain the community structure in complex network, the node with maximum degree in remainder network is first found. Then, some edge cluster coefficients are computed in terms of local information and the agglomeration idea. The tested results on two typical networks show the validity of this algorithm.