{"title":"用于节点和图分类的独特异构增强图对比学习框架","authors":"Qi Shao;Duxin Chen;Wenwu Yu","doi":"10.1109/TNSE.2024.3454993","DOIUrl":null,"url":null,"abstract":"Graph contrastive learning has gained significant attention for its effectiveness in leveraging unlabeled data and achieving superior performance. However, prevalent graph contrastive learning methods often resort to graph augmentation, typically involving the removal of anchor graph structures. This strategy may compromise the essential graph information, constraining the adaptability of contrastive learning approaches across diverse tasks. To overcome this limitation, we introduce a novel augmentation technique for graph contrastive learning: \n<italic>heterogeneous augmentation</i>\n. Through the application of heterogeneous augmentation to homogeneous anchor graphs, our method obviates the need for modifying edges and nodes, preserving the structural integrity of the anchor graph to the fullest extent. The proposed method could become a significant technique in graph augmentation, potentially influencing further research and development in this area. Our work provides a valuable contribution to the advancement of graph contrastive learning methodologies.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5818-5828"},"PeriodicalIF":6.7000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Unique Framework of Heterogeneous Augmentation Graph Contrastive Learning for Both Node and Graph Classification\",\"authors\":\"Qi Shao;Duxin Chen;Wenwu Yu\",\"doi\":\"10.1109/TNSE.2024.3454993\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Graph contrastive learning has gained significant attention for its effectiveness in leveraging unlabeled data and achieving superior performance. However, prevalent graph contrastive learning methods often resort to graph augmentation, typically involving the removal of anchor graph structures. This strategy may compromise the essential graph information, constraining the adaptability of contrastive learning approaches across diverse tasks. To overcome this limitation, we introduce a novel augmentation technique for graph contrastive learning: \\n<italic>heterogeneous augmentation</i>\\n. Through the application of heterogeneous augmentation to homogeneous anchor graphs, our method obviates the need for modifying edges and nodes, preserving the structural integrity of the anchor graph to the fullest extent. The proposed method could become a significant technique in graph augmentation, potentially influencing further research and development in this area. Our work provides a valuable contribution to the advancement of graph contrastive learning methodologies.\",\"PeriodicalId\":54229,\"journal\":{\"name\":\"IEEE Transactions on Network Science and Engineering\",\"volume\":\"11 6\",\"pages\":\"5818-5828\"},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2024-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Network Science and Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10670065/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Network Science and Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10670065/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
A Unique Framework of Heterogeneous Augmentation Graph Contrastive Learning for Both Node and Graph Classification
Graph contrastive learning has gained significant attention for its effectiveness in leveraging unlabeled data and achieving superior performance. However, prevalent graph contrastive learning methods often resort to graph augmentation, typically involving the removal of anchor graph structures. This strategy may compromise the essential graph information, constraining the adaptability of contrastive learning approaches across diverse tasks. To overcome this limitation, we introduce a novel augmentation technique for graph contrastive learning:
heterogeneous augmentation
. Through the application of heterogeneous augmentation to homogeneous anchor graphs, our method obviates the need for modifying edges and nodes, preserving the structural integrity of the anchor graph to the fullest extent. The proposed method could become a significant technique in graph augmentation, potentially influencing further research and development in this area. Our work provides a valuable contribution to the advancement of graph contrastive learning methodologies.
期刊介绍:
The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.