CAJ | 학술논문

重叠社区发现是复杂网络挖掘中的重要任务之一，在恐怖组织识别、蛋白质功能预测、舆情分析等方面具有重要的理论和应用价值。但是，现有的社区挖掘算法很少针对网络中的动态网络社区。针对这一问题文章提出了一种基于APRIORI的重叠社区发现算法来解决动态网络重叠社区的发现问题。该算法首先用APRIORI算法发现网络中用户的关联规则形成候选社区集，在候选社区中按支持度的顺序取出候选社区合成重叠社区，并利用社区间通信概率来计算社区间重叠度，将重叠度过大的社区合并得到最终的重叠社区。结果表明本文算法可以有效发现重叠的网络社区。
중첩사구발현시복잡망락알굴중적중요임무지일，재공포조직식별、단백질공능예측、여정분석등방면구유중요적이론화응용개치。단시，현유적사구알굴산법흔소침대망락중적동태망락사구。침대저일문제문장제출료일충기우APRIORI적중첩사구발현산법래해결동태망락중첩사구적발현문제。해산법수선용APRIORI산법발현망락중용호적관련규칙형성후선사구집，재후선사구중안지지도적순서취출후선사구합성중첩사구，병이용사구간통신개솔래계산사구간중첩도，장중첩도과대적사구합병득도최종적중첩사구。결과표명본문산법가이유효발현중첩적망락사구。
Community discovery is an important task in mining complex networks, and has important theoretical and application value in the terrorist organization identification, protein function prediction, public opinion analysis, etc. However, the existing community mining for the dynamic network is rarely. To address the issues, a new method for detecting the overlapping communities in dynamic networks is proposed. This method bases on the Apriori algorithm, firstly using Apriori algorithm to mine the association relations between users and form a candidate communities set. Then, takes out candidate communities from the candidate communities set in support of order and calculates the communication probability between communities meanwhile merges communities whose communication probability are too high. Finally, the ultimate overlapping communities can be made out. This method provides us with a useful way to detecting the overlapping communities in dynamic networks.