长春理工大学学报(自然科学版)
長春理工大學學報(自然科學版)
장춘리공대학학보(자연과학판)
Journal of Changchun University of Science and Technology
2015年
5期
127-130
,共4页
社区发现%边分类%SVM模型%LFR
社區髮現%邊分類%SVM模型%LFR
사구발현%변분류%SVM모형%LFR
community detection%edge classification%SVM model%LFR
社区发现是复杂网络研究的重要内容,也是分析网络结构的重要途径.分析了社区发现研究中存在的问题,提出了一种基于边分类的SVM模型.通过边顶点相似度和边介数来表示边的特征,从而构造分类函数.利用LFR生成社区结构已知的人工网络,通过人工网络数据训练基于边分类的SVM模型,对分类函数的参数进行估计,利用训练模型对真实网络进行社区分类并通过标准化互信息(NMI)和整体准确度来评价分类效果.实验得到了较高的整体准确度和NMI值.实验表明基于边分类的SVM训练模型对真实网络数据的社区划分有较高的准确度,表明该方法是可行的.
社區髮現是複雜網絡研究的重要內容,也是分析網絡結構的重要途徑.分析瞭社區髮現研究中存在的問題,提齣瞭一種基于邊分類的SVM模型.通過邊頂點相似度和邊介數來錶示邊的特徵,從而構造分類函數.利用LFR生成社區結構已知的人工網絡,通過人工網絡數據訓練基于邊分類的SVM模型,對分類函數的參數進行估計,利用訓練模型對真實網絡進行社區分類併通過標準化互信息(NMI)和整體準確度來評價分類效果.實驗得到瞭較高的整體準確度和NMI值.實驗錶明基于邊分類的SVM訓練模型對真實網絡數據的社區劃分有較高的準確度,錶明該方法是可行的.
사구발현시복잡망락연구적중요내용,야시분석망락결구적중요도경.분석료사구발현연구중존재적문제,제출료일충기우변분류적SVM모형.통과변정점상사도화변개수래표시변적특정,종이구조분류함수.이용LFR생성사구결구이지적인공망락,통과인공망락수거훈련기우변분류적SVM모형,대분류함수적삼수진행고계,이용훈련모형대진실망락진행사구분류병통과표준화호신식(NMI)화정체준학도래평개분류효과.실험득도료교고적정체준학도화NMI치.실험표명기우변분류적SVM훈련모형대진실망락수거적사구화분유교고적준학도,표명해방법시가행적.
The community detection is an important part of the complex network research, and it is also the important way to analyze the network structure. In this paper,the problems existing in the community detection research are ana-lyzed and a kind of SVM model based on the edge classification is proposed. Based on vertex similarity and edge be-tweenness the characteristics of the edge are represented,so the classification function is constructed. The artificial net-work of the known community structure is generated by LFR. Through artificial network data training based on edge classification of SVM model, the parameters of classification function are estimated and the real network community is simulated by using the trained model. The higher overall accuracy and NMI values are got in the experiment. Experi-ments show that the edge classification of SVM trained model have higher accuracy on real network data and the meth-od is effective.