{"title":"自适应高阶图嵌入的半监督分类","authors":"Zhili Ye, Fengge Wu","doi":"10.1109/SERA.2018.8477202","DOIUrl":null,"url":null,"abstract":"The problem of semi-supervised graph node classification is to infer the labels of unlabeled nodes based on a partially labeled graph. Graph embedding is an effective method for this problem, which utilizes the context generated by neighbors' information. Some recent approaches preserve high-order proximity to smooth the features embedded with long-range structure dependency. However, the features generated by high-order proximity may be too smooth to lost individual characteristics. To handle this problem, we propose Adaptive High-Order Graph Embedding (AHOGE), an end-to-end graph neural network that implements embedding and classification in a unified model, to retain individual details when preserving high-order proximity. Inspired by Densely Connected Convolutional Networks (DenseNets), AHOGE adaptively adopts the information of $k^{th}$-order proximity for different $k$, using the techniques of Highway Network. Moreover, we introduce multi-class hinge loss to deal with the hard annotated labels and class overlap. Experiments on three benchmark citation network datasets demonstrate that our approach achieves state-of-the-art performances.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Semi-Supervised Classification with Adaptive High-Order Graph Embedding\",\"authors\":\"Zhili Ye, Fengge Wu\",\"doi\":\"10.1109/SERA.2018.8477202\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of semi-supervised graph node classification is to infer the labels of unlabeled nodes based on a partially labeled graph. Graph embedding is an effective method for this problem, which utilizes the context generated by neighbors' information. Some recent approaches preserve high-order proximity to smooth the features embedded with long-range structure dependency. However, the features generated by high-order proximity may be too smooth to lost individual characteristics. To handle this problem, we propose Adaptive High-Order Graph Embedding (AHOGE), an end-to-end graph neural network that implements embedding and classification in a unified model, to retain individual details when preserving high-order proximity. Inspired by Densely Connected Convolutional Networks (DenseNets), AHOGE adaptively adopts the information of $k^{th}$-order proximity for different $k$, using the techniques of Highway Network. Moreover, we introduce multi-class hinge loss to deal with the hard annotated labels and class overlap. Experiments on three benchmark citation network datasets demonstrate that our approach achieves state-of-the-art performances.\",\"PeriodicalId\":161568,\"journal\":{\"name\":\"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SERA.2018.8477202\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SERA.2018.8477202","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Semi-Supervised Classification with Adaptive High-Order Graph Embedding
The problem of semi-supervised graph node classification is to infer the labels of unlabeled nodes based on a partially labeled graph. Graph embedding is an effective method for this problem, which utilizes the context generated by neighbors' information. Some recent approaches preserve high-order proximity to smooth the features embedded with long-range structure dependency. However, the features generated by high-order proximity may be too smooth to lost individual characteristics. To handle this problem, we propose Adaptive High-Order Graph Embedding (AHOGE), an end-to-end graph neural network that implements embedding and classification in a unified model, to retain individual details when preserving high-order proximity. Inspired by Densely Connected Convolutional Networks (DenseNets), AHOGE adaptively adopts the information of $k^{th}$-order proximity for different $k$, using the techniques of Highway Network. Moreover, we introduce multi-class hinge loss to deal with the hard annotated labels and class overlap. Experiments on three benchmark citation network datasets demonstrate that our approach achieves state-of-the-art performances.