计算机应用研究2013，Vol.30Issue(3)：692-694,3.DOI:10.3969/j.issn.1001-3695.2013.03.012
基于流形学习的社会化媒体网络数据分类
Networked data classification in social media based on manifold learning
摘要
Abstract
Social media provided massive, large-scale heterogeneous networked data. Classification in networked data is a new problem that needed to be solved. Based on latent social dimension model,this paper proposed using Laplacian eigenmaps from manifold learning to extract social dimensions . Experiments show that it is superior to original modularity maximization social dimension model in performance metrics like exact match ratio, micro average and macro average. The algorithm can capture implicit user relations better and analysis Web user behavior better.关键词
流形学习/拉普拉斯特征映射/社会化媒体/网络数据分类/多标签Key words
manifold learning/ Laplacian eigenmaps/ social media/ networked data classification/ multi-label分类
信息技术与安全科学引用本文复制引用
史仍浩,陈秀真,李生红..基于流形学习的社会化媒体网络数据分类[J].计算机应用研究,2013,30(3):692-694,3.基金项目
国家"973"计划资助项目(2010CB731403,2010CB731406) （2010CB731403,2010CB731406）
国家自然科学基金资助项目(61071152,61271316) （61071152,61271316）
国家"十二五"科技支撑计划重点项目(2012BAH38B04) （2012BAH38B04）
