{"title":"非平衡节点分类的双分支图卷积网络","authors":"Xiaoguo Wang, Jiali Chen","doi":"10.1145/3487075.3487162","DOIUrl":null,"url":null,"abstract":"Graph convolutional neural networks (GCNs) have attracted much attention in dealing with various node classification tasks on graphs. Some real-world node classification tasks face the situation that the number of minority class nodes is significantly less than that of majority class nodes. This makes us more concerned about how to effectively solve the problem of imbalanced node classification based on GCNs. To solve this problem, we propose a Dual-branch Graph Convolutional Network framework (D-GCN), which can reduce the dominant effect of majority class on topology aggregation and the negative impact of information differences caused by graph structure reconstruction. This framework achieves the goal of decreasing the possibility of misrecognizing the minority class nodes as majority class and improving the classification performance of minority class nodes. Experiments on several graph datasets demonstrate that D-GCN outperforms representative baselines in solving imbalanced node classification tasks.","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A Dual-branch Graph Convolutional Network on Imbalanced Node Classification\",\"authors\":\"Xiaoguo Wang, Jiali Chen\",\"doi\":\"10.1145/3487075.3487162\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Graph convolutional neural networks (GCNs) have attracted much attention in dealing with various node classification tasks on graphs. Some real-world node classification tasks face the situation that the number of minority class nodes is significantly less than that of majority class nodes. This makes us more concerned about how to effectively solve the problem of imbalanced node classification based on GCNs. To solve this problem, we propose a Dual-branch Graph Convolutional Network framework (D-GCN), which can reduce the dominant effect of majority class on topology aggregation and the negative impact of information differences caused by graph structure reconstruction. This framework achieves the goal of decreasing the possibility of misrecognizing the minority class nodes as majority class and improving the classification performance of minority class nodes. Experiments on several graph datasets demonstrate that D-GCN outperforms representative baselines in solving imbalanced node classification tasks.\",\"PeriodicalId\":354966,\"journal\":{\"name\":\"Proceedings of the 5th International Conference on Computer Science and Application Engineering\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 5th International Conference on Computer Science and Application Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3487075.3487162\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3487075.3487162","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Dual-branch Graph Convolutional Network on Imbalanced Node Classification
Graph convolutional neural networks (GCNs) have attracted much attention in dealing with various node classification tasks on graphs. Some real-world node classification tasks face the situation that the number of minority class nodes is significantly less than that of majority class nodes. This makes us more concerned about how to effectively solve the problem of imbalanced node classification based on GCNs. To solve this problem, we propose a Dual-branch Graph Convolutional Network framework (D-GCN), which can reduce the dominant effect of majority class on topology aggregation and the negative impact of information differences caused by graph structure reconstruction. This framework achieves the goal of decreasing the possibility of misrecognizing the minority class nodes as majority class and improving the classification performance of minority class nodes. Experiments on several graph datasets demonstrate that D-GCN outperforms representative baselines in solving imbalanced node classification tasks.