数据采集与处理
數據採集與處理
수거채집여처리
JOURNAL OF DATA ACQUISITION & PROCESSING
2015年
3期
683-693
,共11页
有向图%社区发现%共社区邻近相似性%带权无向图%重叠社区
有嚮圖%社區髮現%共社區鄰近相似性%帶權無嚮圖%重疊社區
유향도%사구발현%공사구린근상사성%대권무향도%중첩사구
directed graph%community detection%co-community neighboring similarity%weighted undi-rected graph%overlapping community
当前社区发现算法主要是针对无向图研究社区结构,但在实际复杂网络中,链接关系时常表现出非对称性或方向性,比如Twitter的用户关注关系,文献网络的引用关系,网页之间的超链接关系等应用网络。因此,本文依据信息在复杂网络中的传播规律和流动方向性,提出了k‐Path共社区邻近相似性概念及计算方法,用于衡量结点在同一社区的相似性程度,并给出了把有向图转换为带方向权值的无向图的方法。基于带权无向图提出了一种从局部扩展来探测社区的重叠社区发现算法(Local and wave‐like extension algorithm of detecting overlapping community ,LWS‐OCD)。在真实数据集上的实验表明,共社区邻近相似性概念实现了有向到无向的合理转换,而且提高了社区结点的聚集效果,LWS‐OCD算法能够有效地发现带权无向图中的重叠社区。
噹前社區髮現算法主要是針對無嚮圖研究社區結構,但在實際複雜網絡中,鏈接關繫時常錶現齣非對稱性或方嚮性,比如Twitter的用戶關註關繫,文獻網絡的引用關繫,網頁之間的超鏈接關繫等應用網絡。因此,本文依據信息在複雜網絡中的傳播規律和流動方嚮性,提齣瞭k‐Path共社區鄰近相似性概唸及計算方法,用于衡量結點在同一社區的相似性程度,併給齣瞭把有嚮圖轉換為帶方嚮權值的無嚮圖的方法。基于帶權無嚮圖提齣瞭一種從跼部擴展來探測社區的重疊社區髮現算法(Local and wave‐like extension algorithm of detecting overlapping community ,LWS‐OCD)。在真實數據集上的實驗錶明,共社區鄰近相似性概唸實現瞭有嚮到無嚮的閤理轉換,而且提高瞭社區結點的聚集效果,LWS‐OCD算法能夠有效地髮現帶權無嚮圖中的重疊社區。
당전사구발현산법주요시침대무향도연구사구결구,단재실제복잡망락중,련접관계시상표현출비대칭성혹방향성,비여Twitter적용호관주관계,문헌망락적인용관계,망혈지간적초련접관계등응용망락。인차,본문의거신식재복잡망락중적전파규률화류동방향성,제출료k‐Path공사구린근상사성개념급계산방법,용우형량결점재동일사구적상사성정도,병급출료파유향도전환위대방향권치적무향도적방법。기우대권무향도제출료일충종국부확전래탐측사구적중첩사구발현산법(Local and wave‐like extension algorithm of detecting overlapping community ,LWS‐OCD)。재진실수거집상적실험표명,공사구린근상사성개념실현료유향도무향적합리전환,이차제고료사구결점적취집효과,LWS‐OCD산법능구유효지발현대권무향도중적중첩사구。
Most of the previous research on community detection are mainly based on the undirected graph structures .However ,in actual complex networks ,the links relation usually shows the asymmetric char‐acteristic or directionality ,such as citation network of scientific papers ,the one‐way follow relationship on Twitter ,and hyperlinks between web pages .Therefore ,based on the propagation of information and the direction of information transmission ,a k‐Path conception and calculation method for measuring the similarity of co‐community neighboring is presented to weigh possibility of nodes in the same community . Furthermore ,the method of transferring directed graphs into undirected graphs with similarity of weight is presented .Then the local extension algorithm of detecting overlapping community based on weighted undirected graphs is proposed .Several experiments on the real data sets are conducted and analyzed .Ex‐perimental results demonstrate that the k‐Path conception can achieve the reasonable conversion for di‐rected graph and improve the effectiveness of the community gathering nodes .Finally ,the results show that the algorithm can detect the overlapping community effectively .