Zhichang Xia;Xinglin Zhang;Lingyu Liang;Yun Li;Yuejiao Gong
{"title":"用于半监督节点分类的联合图增强技术","authors":"Zhichang Xia;Xinglin Zhang;Lingyu Liang;Yun Li;Yuejiao Gong","doi":"10.1109/TCSS.2024.3373633","DOIUrl":null,"url":null,"abstract":"Semisupervised node classification is a prevalent task on graphs, which involves predicting the labels of unlabeled nodes based on limited labeled data available. At present, centralized approaches to training models for this task are unsustainable due to the increasing demand for computational power, storage capacity, and privacy. An approach of potential is federated graph learning (FGL), which allows multiple clients to collaborate on learning a model while maintaining data privacy. However, current methods suffer from the inability to consider the topology of the graph data and inadequate use of unlabeled data. To address these issues, we propose federated graph augmentation (FedGA) by combining graph neural network (GNN) models to utilize similar topologies existing in different client graphs and augment the client data. Furthermore, we develop FedGA-L based on FedGA, which integrates pseudolabeling and label-injection to improve the utilization of unlabeled data. FedGA-L allows pseudolabels to be used as additional information to enhance data augmentation and further improve the accuracy of node classification. We evaluate the effectiveness of FedGA and FedGA-L through experiments on multiple datasets. The results demonstrate improved accuracy in solving typical classification tasks and their compatibility with a variety of federated learning (FL) frameworks. On widely recognized datasets for graph learning, we achieve an accuracy improvement of 5%–7% compared to vanilla federated learning algorithms.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Federated Graph Augmentation for Semisupervised Node Classification\",\"authors\":\"Zhichang Xia;Xinglin Zhang;Lingyu Liang;Yun Li;Yuejiao Gong\",\"doi\":\"10.1109/TCSS.2024.3373633\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Semisupervised node classification is a prevalent task on graphs, which involves predicting the labels of unlabeled nodes based on limited labeled data available. At present, centralized approaches to training models for this task are unsustainable due to the increasing demand for computational power, storage capacity, and privacy. An approach of potential is federated graph learning (FGL), which allows multiple clients to collaborate on learning a model while maintaining data privacy. However, current methods suffer from the inability to consider the topology of the graph data and inadequate use of unlabeled data. To address these issues, we propose federated graph augmentation (FedGA) by combining graph neural network (GNN) models to utilize similar topologies existing in different client graphs and augment the client data. Furthermore, we develop FedGA-L based on FedGA, which integrates pseudolabeling and label-injection to improve the utilization of unlabeled data. FedGA-L allows pseudolabels to be used as additional information to enhance data augmentation and further improve the accuracy of node classification. We evaluate the effectiveness of FedGA and FedGA-L through experiments on multiple datasets. The results demonstrate improved accuracy in solving typical classification tasks and their compatibility with a variety of federated learning (FL) frameworks. On widely recognized datasets for graph learning, we achieve an accuracy improvement of 5%–7% compared to vanilla federated learning algorithms.\",\"PeriodicalId\":13044,\"journal\":{\"name\":\"IEEE Transactions on Computational Social Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Computational Social Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10486855/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, CYBERNETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Social Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10486855/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
Federated Graph Augmentation for Semisupervised Node Classification
Semisupervised node classification is a prevalent task on graphs, which involves predicting the labels of unlabeled nodes based on limited labeled data available. At present, centralized approaches to training models for this task are unsustainable due to the increasing demand for computational power, storage capacity, and privacy. An approach of potential is federated graph learning (FGL), which allows multiple clients to collaborate on learning a model while maintaining data privacy. However, current methods suffer from the inability to consider the topology of the graph data and inadequate use of unlabeled data. To address these issues, we propose federated graph augmentation (FedGA) by combining graph neural network (GNN) models to utilize similar topologies existing in different client graphs and augment the client data. Furthermore, we develop FedGA-L based on FedGA, which integrates pseudolabeling and label-injection to improve the utilization of unlabeled data. FedGA-L allows pseudolabels to be used as additional information to enhance data augmentation and further improve the accuracy of node classification. We evaluate the effectiveness of FedGA and FedGA-L through experiments on multiple datasets. The results demonstrate improved accuracy in solving typical classification tasks and their compatibility with a variety of federated learning (FL) frameworks. On widely recognized datasets for graph learning, we achieve an accuracy improvement of 5%–7% compared to vanilla federated learning algorithms.
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.