采用群体信息的二部图链接预测方法

doi:10.3969/j.issn.1000-3428.2016.10.033

摘要/Abstract

摘要： 二部图包含2种不同类型的节点且链接只存在于不同类型的节点之间,因此,许多适用于普通单部图的链接预测方法无法直接用于二部图中。另外,群体信息对提高链接预测的准确率有重要意义,但缺乏相关研究。为此,提出一种采用群体信息的二部图链接预测方法。将链接预测视为机器学习的分类问题,通过对二部图投影,抽取二部图中节点对样本的局部结构属性,并运用群体检测技术抽取节点对样本的群体属性,并把局部结构属性和群体属性一起作为节点对相似度的度量标准,在监督学习框架中进行训练和预测。在现实数据集MovieLens中的实验结果表明,群体信息的引入能有效提高二部图链接预测方法的准确率,改善推荐性能。

关键词: 二部图, 链接预测, 监督学习, 群体检测, 推荐系统

Abstract: Since bipartite graph contains two different types of nodes and links only exist between different types of nodes,most link prediction methods for common single graph cannot be applied to bipartite graphs directly.In addition,the community information does not draw enough attention though it is important to improve the accuracy of link prediction.So a bipartite link prediction method using community information is developed.The method regards link prediction as a classification problem in machine learning.The local structural properties of node pair instances in a bipartite graph are extracted by the projection of the bipartite graph,together with community properties of instances by exploiting community detection techniques.Then local structural properties and community properties are used as similarity measurements of node pair instances.Training and prediction are conducted in a supervised learning process.Experimental results in a real dataset MovieLens show that the use of community information improves the link prediction accuracy and recommendation performance.

Key words: bipartite graph, link prediction, supervised learning, community detection, recommendation system

中图分类号:

TP18

蔡小雨,陈可佳,安琛. 采用群体信息的二部图链接预测方法[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2016.10.033.

CAI Xiaoyu,CHEN Kejia,AN Chen. Bipartite Graph Link Prediction Method Using Community Information[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2016.10.033.

http://www.ecice06.com/CN/Y2016/V42/I10/187

参考文献

参考文献［1］Liben N D,Kleinberg J.The Link Prediction Problem for Social Networks［J］.Science Technology,2007,58(7):1019-1031. ［2］陈可佳,韩京宇,郑正中,等.主动学习在通信网络推荐系统中的应用［J］.计算机应用,2012,32(11):3038-3041. ［3］Kunegis J,Luca E W D,Albayrak S.The Link Prediction Problem in Bipartite Networks［C］//Proceedings of the 13th International Conference on Information Processing and Management of Uncertainty.Berlin,Germany:Springer,2010:380-389. ［4］Rendle S,Schmidt T L.Online-updating Regularized Kernel Matrixfactorization Models for Large-scale Recom-mender Systems［C］//Proceedings of ACM Conference on Recommender Systems.New York,USA:ACM Press,2008:251-258. ［5］Almosallam I A,Shang Yi.A New Adaptive Framework for Collaborative Filtering Prediction［C］//Proceedings of IEEE Congress on Evolutionary Computation.Washington D.C.,USA:IEEE Press,2008:2725 - 2733. ［6］Hasan M A,Chaoji V,Salem S,et al.Link Prediction Using Supervised Learning［C］//Proceedings of SDM Workshop on Link Analysis Counterterrorism and Security.Berlin,Germany:Springer,2006:344-360. ［7］Benchettara N,Kanawati R,Rouveirol C.Supervised Machine Learning Applied to Link Prediction in Bipartite Social Networks［C］//Proceedings of International Con-ference on Advances in Social Networks Analysis and Mining.Washington D.C.,USA:IEEE Press,2010:326-330. ［8］Chang Yangjui,Kao Hongyu.Link Prediction in a Bipartite Network Using Wikipedia Revision Information［C］//Pro-ceedings of Conference on Technologies and Applications of Artificial Intelligence.Washington D.C.,USA:IEEE Press,2012:50-55. ［9］Yajima Y.One-class Support Vector Machines for Recommendation Tasks［C］//Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining.Berlin,Germany:Springer,2006:230-239. ［10］Li Xin,Chen Hsin-chun.Recommendation as Link Prediction in Bipartite Graphs:A Graph Kernel-based Machine Learning Approach［J］.Decision Support Systems,2009,54(2):213-216. ［11］Pujari M,Kanawati R.Tag Recommendation by Link Prediction Based on Supervised Machine Learning［C］//Proceedings of the 6th International Conference on Weblogs and Social Media.Dublin,Ireland:AAAI Press,2012:547-550. ［12］Allali O,Magnien C,Latapy M.Internal Link Predi-ction:A New Approach for Predicting Links in Bipartite Graphs［J］.Intelligent Data Analysis,2013,17(1):5-25. ［13］Chinta K K,Clark K,Mani A.CS224W Project Final Report Supervised Link Prediction in Bipartite Networks［EB/OL］.(2014-11-13).http://snap.stanford.edu/class/cs224w-2014/projects2014/cs224w-82-final.pdf. ［14］Dietterich T G,Domingos P,Getoor L,et al.Structured Machine Learning:The Next Ten Years［J］.Machine Learning,2008,73(1):3-23. ［15］Macqueen J.Some Methods for Classification and Analysis of Multivariate Observations［C］//Proceedings of the 5th Berkeley Symposium on Mathematical Stati-stics and Probability.Berkeley,USA:University of California Press,1967:281-297. ［16］Grtner T.A Survey of Kernels for Structured Data［J］.ACM SIGKDD Explorations Newsletter,2003,5(1):49-58. ［17］Wu A Y,Garland M,Han Jiawei.Mining Scale-free Networks Using Geodesic Clustering［C］//Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining.New York,USA:ACM Press,2004:719-724. ［18］Xu Feng,Zhao Jichang,Xu Ke.Link Prediction in Com-plex Networks:A Clustering Perspective［J］.Physics of Condensed Matter,2012,85(1):4-4. ［19］Soundarajan S,Hopcroft J.Using Community Infor-mation to Improve the Precision of Link Prediction Methods［C］//Proceedings of the 21st International Con-ference Companion on World Wide Web.New York,USA:ACM Press,2012:607-608. ［20］Valverde R J,Andrade L A.Structural Link Predi-ction Using Community Information on Twitter［C］//Proceedings of the 4th International Conference on Com-putational Aspects of Social Networks.Washington D.C.,USA:IEEE Press,2012:132-137. ［21］Hand D J,Till R J.A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems［J］.Machine Learning,2001,45(2):171-186. 编辑顾逸斐

[1]	王书朋, 何引弟. 融合特征注意力机制的非均匀光照图像增强算法[J]. 计算机工程, 2023, 49(8): 232-239.
[2]	马娜, 温廷新, 贾旭, 李晓会. 复杂光照条件下自适应的车脸重识别模型[J]. 计算机工程, 2023, 49(8): 275-282, 290.
[3]	陈仲磊, 伊鹏, 陈祥, 胡涛. 基于集成学习的系统调用实时异常检测框架[J]. 计算机工程, 2023, 49(6): 162-169,179.
[4]	许凤, 杨兴耀, 于炯, 李梓杨, 李晨瑜, 张君. 小波卷积增强的对比学习推荐算法[J]. 计算机工程, 2023, 49(5): 105-111,121.
[5]	唐彦, 卢镘旭. 基于知识图谱与深度涟漪网络的推荐系统[J]. 计算机工程, 2023, 49(5): 63-72,80.
[6]	潘乐, 李弼程, 万旺, 曾荣燊. 基于关系强度理论与反馈机制的信息传播动态网络表示[J]. 计算机工程, 2023, 49(2): 246-253.
[7]	李盼, 解庆, 李琳, 刘永坚. 知识增强的图神经网络序列推荐模型[J]. 计算机工程, 2023, 49(2): 70-80.
[8]	何悦, 陈广胜, 景维鹏, 徐泽堃. 基于深度多相似性哈希方法的遥感图像检索[J]. 计算机工程, 2023, 49(2): 206-212.
[9]	王曙燕, 郭睿涵, 孙家泽. 基于图对比学习的MOOC推荐方法[J]. 计算机工程, 2023, 49(1): 57-64,72.
[10]	蒋雪瑶, 力维辰, 刘井平, 李直旭, 肖仰华. 基于多模态模式迁移的知识图谱实体配图[J]. 计算机工程, 2022, 48(8): 70-76.
[11]	佘朝阳, 严馨, 徐广义, 陈玮, 邓忠莹. 融合数据增强与半监督学习的药物不良反应检测[J]. 计算机工程, 2022, 48(6): 314-320.
[12]	张恒, 陈晓红, 蓝宇翔, 李舜酩. 基于深度学习的监督型典型相关分析[J]. 计算机工程, 2022, 48(5): 222-228.
[13]	唐佳敏, 韩华, 黄丽. 行人再识别中基于无监督学习的粗细粒度特征提取[J]. 计算机工程, 2022, 48(4): 269-275,283.
[14]	陈恒, 王思懿, 李冠宇, 祁瑞华, 杨晨, 王维美. 基于四元数胶囊网络的知识图谱补全模型[J]. 计算机工程, 2022, 48(2): 40-46,64.
[15]	胡彬, 王晓军, 张雷. 一种半监督对抗鲁棒模型无关元学习方法[J]. 计算机工程, 2022, 48(12): 112-118.

选择文件类型/文献管理软件名称

选择包含的内容