[1]刘宇廷,倪颖杰.融合知识迁移学习的微博社团检测模型构建[J].计算机技术与发展,2018,28(09):11-15.[doi:10.3969/j.issn.1673-629X.2018.09.003]
　LIU Yu-ting,NI Ying-jie.Construction of Weibo Community Detection Model with Knowledge Transfer Learning[J].,2018,28(09):11-15.[doi:10.3969/j.issn.1673-629X.2018.09.003]
点击复制

融合知识迁移学习的微博社团检测模型构建()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年09期
页码:
11-15
栏目:
智能、算法、系统工程
出版日期:
2018-09-10

文章信息/Info

Title:
Construction of Weibo Community Detection Model with Knowledge Transfer Learning
文章编号:
1673-629X(2018)09-0011-05
作者:
刘宇廷;  倪颖杰
江南计算技术研究所,江苏 无锡,214083
Author(s):
LIU Yu-ting; NI Ying-jie
Jiangnan Institute of Computing Technology,Wuxi 214083,China
关键词:
迁移学习;  机器学习;  社交网络;  社团检测
Keywords:
transfer learning; machine learning; social networks; community detection
分类号:
TP181
DOI:
10.3969/j.issn.1673-629X.2018.09.003
文献标志码:
A
摘要:
传统社团检测算法大多基于网络拓扑结构,没有充分利用网络节点的标签等信息,所以无法合理地解释得到的社团结构.微博、Facebook、Twitter等社交媒体网络增长迅速,用户标签通常不完整,应用传统机器学习模型补全标签通常需要大量训练样本,这种模式需要人工标注训练数据,时间周期长、泛化能力差.将迁移学习理论应用到这类任务中,可以避免人工标注损耗、缩短训练时间,所以针对新浪微博数据特点,提出一种融合知识迁移学习的微博社团结构检测模型(community structure inference model with knowledge transfer learning,KTL-CSIM).社团结构检测模型基于度数相关的随机块模型,建立基于拓扑结构与节点信息的似然概率模型.文本向量化模型基于知识迁移模型将源领域知识迁移到目标领域微博数据上,得到目标领域文本向量.这种方法不需要人工标注数据,有效减少了模型训练时间,提高了泛化能力.
Abstract:
Most of the traditional community detection algorithms are based on the network topological structure,and do not make full use of node information,so it is impossible to interpret the community structure reasonable. Social media network like Sina Weibo,Facebook and Twitter grow rapidly,but the user tag is usually incomplete. The application of traditional machine learning model to complete the tag usually requires a large number of training samples,which requires manual labeling of training data with a long time period and poor gen- eralization. And applying the theory of transfer learning to such tasks can not only avoid the manual tagging of data,but also shorten the training time. Therefore,based on the characteristics of Sina Weibo data,we propose a new method of community structure inference model with knowledge transfer learning (KTL-CSIM). The model is based on degree correlated stochastic block model. Likelihood probability model is build upon network topology and node information. Word embedding model based on knowledge transfer learning model transfers the knowledge from the source domain to the target domain. This method does not require manual tagging data,which ef- fectively reduces the training time and improves the generalization as well.

相似文献/References:

[1]李 勇,刘战东,张海军.跨项目软件缺陷预测方法研究综述[J].计算机技术与发展,2020,30(03):98.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 019]
　LI Yong,LIU Zhan-dong,ZHANG Hai-jun.Review on Cross-project Software Defects Prediction Methods[J].,2020,30(09):98.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 019]
[2]武苏雯,赵慧杰,刘 鑫,等.基于迁移学习的图像分类在诗词中的应用研究[J].计算机技术与发展,2021,31(07):215.[doi:10. 3969 / j. issn. 1673-629X. 2021. 07. 036]
　WU Su-wen,ZHAO Hui-jie,LIU Xin,et al.Research on Application of Image Classification Based onTransfer Learning in Poetry[J].,2021,31(09):215.[doi:10. 3969 / j. issn. 1673-629X. 2021. 07. 036]
[3]娄丰鹏,吴迪,荆晓远,等.增加度量元的迁移学习跨项目软件缺陷预测[J].计算机技术与发展,2018,28(07):103.[doi:10.3969/ j. issn.1673-629X.2018.07.022]
　LOU Feng-peng,WU Di,JING Xiao-yuan,et al.Cross-project Software Defect Prediction Based on Transfer Learning with Metrics[J].,2018,28(09):103.[doi:10.3969/ j. issn.1673-629X.2018.07.022]
[4]张洋洋,荆晓远,吴飞.基于迁移学习的跨项目软件缺陷预测[J].计算机技术与发展,2018,28(12):82.[doi:10．3969/j． issn．1673－629X．2018．12．018]
　ZHANG Yangyang,JING Xiaoyuan,WU Fei.Cross－project Software Defect Prediction Based on Transfer Learning[J].,2018,28(09):82.[doi:10．3969/j． issn．1673－629X．2018．12．018]
[5]王泽泓,刘厚泉.基于迁移学习与自适应特征融合的建筑物识别[J].计算机技术与发展,2019,29(12):40.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 007]
　WANG Ze-hong,LIU Hou-quan.Building Recognition Based on Transfer Learning and Adaptive Feature Fusion[J].,2019,29(09):40.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 007]
[6]易 未,郑沫利,赵艳轲,等.基于小样本 SVR 的迁移学习及其应用[J].计算机技术与发展,2020,30(02):47.[doi:10. 3969 / j. issn. 1673-629X. 2020. 02. 010]
　YI Wei,ZHENG Mo-li,ZHAO Yan-ke,et al.Transfer Learning Based on Support Vector Regression Model for Small Sample Data and Its Applications[J].,2020,30(09):47.[doi:10. 3969 / j. issn. 1673-629X. 2020. 02. 010]
[7]王新美,丁爱玲,雷梦宁,等.基于 CNN 和 SVM 融合的交通标志识别[J].计算机技术与发展,2020,30(06):7.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 002]
　WANG Xin-mei,DING Ai-ling,LEI Meng-ning,et al.Traffic Sign Recognition Based on Combination of CNN and SVM[J].,2020,30(09):7.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 002]
[8]龚 安,井晓萌.多卷积神经网络模型融合的农作物病害图像识别[J].计算机技术与发展,2020,30(08):134.[doi:10. 3969 / j. issn. 1673-629X. 2020. 08. 023]
　GONG An,JING Xiao-meng.Image Recognition of Crop Diseases Based on Multi-convolution Neural Network Model Ensemble[J].,2020,30(09):134.[doi:10. 3969 / j. issn. 1673-629X. 2020. 08. 023]
[9]龚 安,郭文婷.基于卷积神经网络的皮肤癌识别方法[J].计算机技术与发展,2020,30(10):167.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 030]
　GONG An,GUO Wen-ting.Skin Cancer Image Classification Method Based on Convolutional Neural Network[J].,2020,30(09):167.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 030]
[10]张泽宇,郭 斌,张太红*.基于 DCNN 的马匹图像分割算法研究[J].计算机技术与发展,2020,30(10):210.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 037]
　ZHANG Ze-yu,GUO Bin,ZHANG Tai-hong.Research on Horse Image Segmentation Algorithm Based on DCNN[J].,2020,30(09):210.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 037]

更新日期/Last Update: 2018-09-10