山东大学学报(工学版)
山東大學學報(工學版)
산동대학학보(공학판)
JOURNAL OF SHANDONG UNIVERSITY(ENGINEERING SCIENCE)
2014年
6期
26-31
,共6页
社会网络%链接预测%潜在狄利克雷分配%网络演化%主题模型
社會網絡%鏈接預測%潛在狄利剋雷分配%網絡縯化%主題模型
사회망락%련접예측%잠재적리극뢰분배%망락연화%주제모형
social network%link prediction%Latent Dirichlet Allocation%network evolution%topic model
针对传统社会网络链接预测方法忽视节点文本内容的问题,提出一种基于潜在狄利克雷分配(Latent Dirichlet Allocation,LDA)主题模型的协作演化链接预测算法。算法利用LDA模型,对节点的文本内容进行分析,提取出每个节点的主题分布向量,利用分布向量的点积来衡量节点文本的相似性;然后将节点文本内容相似性矩阵与节点邻接矩阵相加,在此基础上计算节点之间的相似性;最后选取相似性最高的k个节点作为预测结果。实验结果表明该算法在网络图稀疏的情况下有较好的效果。
針對傳統社會網絡鏈接預測方法忽視節點文本內容的問題,提齣一種基于潛在狄利剋雷分配(Latent Dirichlet Allocation,LDA)主題模型的協作縯化鏈接預測算法。算法利用LDA模型,對節點的文本內容進行分析,提取齣每箇節點的主題分佈嚮量,利用分佈嚮量的點積來衡量節點文本的相似性;然後將節點文本內容相似性矩陣與節點鄰接矩陣相加,在此基礎上計算節點之間的相似性;最後選取相似性最高的k箇節點作為預測結果。實驗結果錶明該算法在網絡圖稀疏的情況下有較好的效果。
침대전통사회망락련접예측방법홀시절점문본내용적문제,제출일충기우잠재적리극뢰분배(Latent Dirichlet Allocation,LDA)주제모형적협작연화련접예측산법。산법이용LDA모형,대절점적문본내용진행분석,제취출매개절점적주제분포향량,이용분포향량적점적래형량절점문본적상사성;연후장절점문본내용상사성구진여절점린접구진상가,재차기출상계산절점지간적상사성;최후선취상사성최고적k개절점작위예측결과。실험결과표명해산법재망락도희소적정황하유교호적효과。
To address the problem of ignoring the text contents of nodes in social network link prediction methods,a La-tent Dirichlet Allocation(LDA)-based collaborative evolutionary link prediction algorithm was proposed.The algorithm used LDA model to analyze the text content and abstracted a topic distribution vector for each node;The product of the topic distribution vectors was adopted to measure the similarity between the nodes′contents;Afterwards,the content similarity matrix was added to the adjacency matrix and the similarities between the nodes were computed consequently;At last,k most similar nodes were selected as the prediction result.The experimental results showed that the proposed algorithm achieved good prediction performance in sparse networks.