事件分类:使用DeepWalk学习的基线
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摘要
提出一种利用DeepWalk对不同社交网络上发布的事件进行分类的方法。该算法的基本思想是基于网络表示学习的DeepWalk方法,将随机游走得到的节点序列当作句子,从截断的随机游走序列中得到网络的局部信息,再通过局部信息来学习节点的潜在表示。然后利用机器学习中的逻辑回归算法进行多标签的分类。收集了一个名为Flickr-WikiYouTube的事件数据集,用于事件的分类,其中数据同时包含了三个不同的社交网络(Flickr,Wiki和YouTube),与现有的网络表示学习数据集中只拥有单一的社交网络不同。在Flickr-Wiki-YouTube事件数据集上进行实验,从实验结果中验证了构建图的合理性以及这种思想的可行性,取得了显著的效果。
This paper proposes a method for classifying news media event reports using DeepWalk.The basic idea of the algorithm is based on the DeepWalk method of network representation learning.The node sequence obtained by random walk is taken as a sentence,the local information of the network is obtained from the truncated random walk sequence,and the potential representation of the node is learned through local information.Then use the logistic regression algorithm in machine learning to classify multiple tags.This paper conducts experiments on Flickr-Wiki-YouTube event dataset.
This paper proposes a method for classifying news media event reports using DeepWalk.The basic idea of the algorithm is based on the DeepWalk method of network representation learning.The node sequence obtained by random walk is taken as a sentence,the local information of the network is obtained from the truncated random walk sequence,and the potential representation of the node is learned through local information.Then use the logistic regression algorithm in machine learning to classify multiple tags.This paper conducts experiments on Flickr-Wiki-YouTube event dataset.
引文
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[2]J Tang,M Qu,M Wang,et al.LINE:Large-scale information network embedding[C]∥Proceedings of the 24th International Conference on World Wide Web,2015:1067-1077
[3]齐金山,梁循.大规模复杂信息网络表示学习:概念,方法与挑战[J].计算机学报,2018(10)
[4]W L Hamilton,R Ying,J Leskovec.Representation Learning on Graphs:Methods and Applications[J].IEEE Data Engineering Bulletin,2017
[5]X Wang,P Cui,J Wang,et al.Community preserving network embedding[C]∥AAAI, 2017:203-209
[6]F Nie,W Zhu,X Li.Unsupervised large graph embedding for asymmetric proximity[C]∥AAAI, 2017:2422-2428
[7]C Zhou,Y Liu,X Liu,et al.Scalable graph embedding for asymmetric proximity[C]∥AAAI, 2017:2942-2948
[8]X Wei,L Xu,B Cao,et al.Cross view link prediction by learning noise-resilient representation consensus[C]∥WWW,2017:1611-1619
[9]P Goyal,E Ferrara.Graph Embedding Techniques, Applications,and Performance:A Survey[J].Knowledge-Based Systems, 2018,151:1-230
[10]H Y Cai,V W Zheng,K Chang.A Comprehensive Survey of1Graph Embedding:Problems,Technologys and Applications[J].IEEE Transactions on Knowledge and Data Engineering,2018:1
[11]P Hoff,A E Raftery,M S Handcock.Latent space approaches to social network analysis[J].JASA,2002,97(406):1090-1098
[12]W Zachary.An information flow model for conflict and fission in small groups[J].Journal of anthropological research,1977,33(4):452-473
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[15]T Mikolov,I Sutskever,K Chen,et al.Distributed representation of words and phrases and their compositionality[C]∥Advances in Nerual Information Processing Systems,2013:3111-3119