{"title":"异构信息网络学习的自监督节点-超边嵌入","authors":"Mengran Li;Yong Zhang;Wei Zhang;Yi Chu;Yongli Hu;Baocai Yin","doi":"10.1109/TBDATA.2023.3275374","DOIUrl":null,"url":null,"abstract":"The exploration of self-supervised information mining of heterogeneous datasets has gained significant traction in recent years. Heterogeneous graph neural networks (HGNNs) have emerged as a highly promising method for handling heterogeneous information networks (HINs) due to their superior performance. These networks leverage aggregation functions to convert pairwise relations-based features from raw heterogeneous graphs into embedding vectors. However, real-world HINs contain valuable higher-order relations that are often overlooked but can provide complementary information. To address this issue, we propose a novel method called \n<bold>S</b>\nelf-supervised \n<bold>N</b>\nodes-\n<bold>H</b>\nyperedges \n<bold>E</b>\nmbedding (SNHE), which leverages hypergraph structures to incorporate higher-order information into the embedding process of HINs. Our method decomposes the raw graph structure into snapshots based on various meta-paths, which are then transformed into hypergraphs to aggregate high-order information within the data and generate embedding representations. Given the complexity of HINs, we develop a dual self-supervised structure that maximizes mutual information in the enhanced graph data space, guides the overall model update, and reduces redundancy and noise. We evaluate our proposed method on various real-world datasets for node classification and clustering tasks, and compare it against state-of-the-art methods. The experimental results demonstrate the efficacy of our method. Our code is available at \n<uri>https://github.com/limengran98/SNHE</uri>\n.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"9 4","pages":"1210-1224"},"PeriodicalIF":7.5000,"publicationDate":"2023-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Self-Supervised Nodes-Hyperedges Embedding for Heterogeneous Information Network Learning\",\"authors\":\"Mengran Li;Yong Zhang;Wei Zhang;Yi Chu;Yongli Hu;Baocai Yin\",\"doi\":\"10.1109/TBDATA.2023.3275374\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The exploration of self-supervised information mining of heterogeneous datasets has gained significant traction in recent years. Heterogeneous graph neural networks (HGNNs) have emerged as a highly promising method for handling heterogeneous information networks (HINs) due to their superior performance. These networks leverage aggregation functions to convert pairwise relations-based features from raw heterogeneous graphs into embedding vectors. However, real-world HINs contain valuable higher-order relations that are often overlooked but can provide complementary information. To address this issue, we propose a novel method called \\n<bold>S</b>\\nelf-supervised \\n<bold>N</b>\\nodes-\\n<bold>H</b>\\nyperedges \\n<bold>E</b>\\nmbedding (SNHE), which leverages hypergraph structures to incorporate higher-order information into the embedding process of HINs. Our method decomposes the raw graph structure into snapshots based on various meta-paths, which are then transformed into hypergraphs to aggregate high-order information within the data and generate embedding representations. Given the complexity of HINs, we develop a dual self-supervised structure that maximizes mutual information in the enhanced graph data space, guides the overall model update, and reduces redundancy and noise. We evaluate our proposed method on various real-world datasets for node classification and clustering tasks, and compare it against state-of-the-art methods. The experimental results demonstrate the efficacy of our method. Our code is available at \\n<uri>https://github.com/limengran98/SNHE</uri>\\n.\",\"PeriodicalId\":13106,\"journal\":{\"name\":\"IEEE Transactions on Big Data\",\"volume\":\"9 4\",\"pages\":\"1210-1224\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2023-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Big Data\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10123057/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Big Data","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10123057/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Self-Supervised Nodes-Hyperedges Embedding for Heterogeneous Information Network Learning
The exploration of self-supervised information mining of heterogeneous datasets has gained significant traction in recent years. Heterogeneous graph neural networks (HGNNs) have emerged as a highly promising method for handling heterogeneous information networks (HINs) due to their superior performance. These networks leverage aggregation functions to convert pairwise relations-based features from raw heterogeneous graphs into embedding vectors. However, real-world HINs contain valuable higher-order relations that are often overlooked but can provide complementary information. To address this issue, we propose a novel method called
S
elf-supervised
N
odes-
H
yperedges
E
mbedding (SNHE), which leverages hypergraph structures to incorporate higher-order information into the embedding process of HINs. Our method decomposes the raw graph structure into snapshots based on various meta-paths, which are then transformed into hypergraphs to aggregate high-order information within the data and generate embedding representations. Given the complexity of HINs, we develop a dual self-supervised structure that maximizes mutual information in the enhanced graph data space, guides the overall model update, and reduces redundancy and noise. We evaluate our proposed method on various real-world datasets for node classification and clustering tasks, and compare it against state-of-the-art methods. The experimental results demonstrate the efficacy of our method. Our code is available at
https://github.com/limengran98/SNHE
.
期刊介绍:
The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.