图嵌入学习研究综述:从简单图到复杂图

doi:10.11896/jsjkx.250300081

摘要/Abstract

摘要： 图数据作为一种具有强大表达能力的数据类型,因具有复杂的结构而难以高效建模。如何有效捕捉其中的内在信息,成为一个富有挑战性的问题。图嵌入方法将高维稀疏的图映射为低维稠密的特征向量,同时保留图的结构信息,已经引起了广泛关注。然而,现有综述对图嵌入方法的总结不够全面,尤其对复杂图嵌入的关注较少,导致处理多样化图数据的研究现状未能得到系统梳理。对此,从简单图到复杂图,对图嵌入学习方法进行了系统综述。首先,给出了各种类型的图和图嵌入的常见定义;其次,系统地归纳了简单图上的嵌入方法,包括浅层和深度图嵌入方法;然后,按照图的种类,总结了复杂图上的嵌入方法,重点介绍深度嵌入技术在动态图、异质图、多重图和超图等复杂图结构中的应用,以弥补现有文献对复杂图结构研究关注较少的不足;最后,讨论了图嵌入技术的实际应用场景,并展望了未来的发展方向。

关键词: 图嵌入, 图表示, 深度学习, 神经网络, 复杂图

Abstract: Graph data,as a data type with strong expressive power,is difficult to model efficiently due to its complex structure.How to effectively capture its intrinsic information has become a challenging problem.Graph embedding methods have received increasing attention by mapping high-dimensional sparse graphs into low-dimensional dense feature vectors,while preserving the structural information of graphs.However,the existing reviews do not summarize the graph embedding methods comprehensively enough,especially paying less attention to complex graph embedding,which leads to the failure to systematically sort out the current status of research on graph embedding in dealing with diverse graph data.Therefore,this paper presents a systematic review of graph embedding learning methods from simple to complex graphs.Firstly,it gives the common definitions of various types of graphs and graph embedding.Secondly,it systematically summarizes the embedding methods on simple graphs,including shallow and deep embedding methods.Then,it summarizes the embedding methods on complex graphs according to the types of graphs,focusing on the application of deep embedding techniques in complex graph structures such as dynamic graphs,heterogeneous graphs,multiplex graphs,and hypergraphs,to fill the gaps in the existing literature that is insufficiently researched on complex graph structures.Finally,it discusses the practical application scenarios of graph embedding techniques,and looks forward to the future development directions.

Key words: Graph embedding, Graph representation, Deep learning, Neural network, Complex graph

中图分类号:

TP181

黄苗苗, 王慧颖, 王梅霞, 王业江, 赵宇海. 图嵌入学习研究综述:从简单图到复杂图[J]. 计算机科学, 2026, 53(1): 58-76. https://doi.org/10.11896/jsjkx.250300081

HUANG Miaomiao, WANG Huiying, WANG Meixia, WANG Yejiang , ZHAO Yuhai. Review of Graph Embedding Learning Research:From Simple Graph to Complex Graph[J]. Computer Science, 2026, 53(1): 58-76. https://doi.org/10.11896/jsjkx.250300081

参考文献

[1]SHARMA K,LEE Y C,NAMBI S,et al.A survey of graph neu-ral networks for social recommender systems[J].ACM Computing Surveys,2024,56(10):1-34.
[2]CUI G Q,ZHOU J,YANG C,et al.Adaptive graph encoder for attributed graph embedding[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York:ACM,2020:976-985.
[3]ZHOU B X,ZHENG L R,WU B H,et al.Protein engineering with lightweight graph denoising neural networks[J].Journal of Chemical Information and Modeling,2024,64(9):3650-3661.
[4]FU N,NI W W,HOU L H,et al.Community detection in de-centralized social networks with local differential privacy[J].Information Sciences,2024,661:120164.
[5]ZHANG Q J,XU Y D.Knowledge graph embedding with inverse function representation for link prediction[J].Engineering Applications of Artificial Intelligence,2024,127:107225.
[6]LI S,ZAIDI N A,DU M J,et al.Property graph representation learning for node classification[J].Knowledge and Information Systems,2024,66(1):237-265.
[7]CAO S S,LU W,XU Q K.GraRep:Learning graph representations with global structural information[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management.New York:ACM,2015:891-900.
[8]OU M D,CUI P,PEI J,et al.Asymmetric transitivity preserving graph embedding[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery & Data Mi-ning.New York:ACM,2016:1105-1114.
[9]PEROZZI B,AL-RFOU R,SKIENA S.DeepWalk:Online lear-ning of social representations[C]//Proceedings the 20th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York:ACM,2014:701-710.
[10]GROVER A,LESKOVEC J.node2vec:Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery & Data Mi-ning.New York:ACM,2016:855-864.
[11]DONG Y,CHAWLA N V,SWAMI A.metapath2vec:Scalable representation learning for heterogeneous networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York:ACM,2017:135-144.
[12]MIKOLOV T.Efficient estimation of word representations invector space[J].arXiv:1301.3781,2013.
[13]CAO S S,LU W,XU Q K.Deep neural networks for learning graph representations[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence.Palo Alto,CA:AAAI,2016:1145-1152.
[14]BRUNA J,ZAREMBA W,SZLAM A,et al.Spectral networks and locally connected networks on graphs[J].arXiv:1312.6203,2013.
[15]YU W C,ZHENG C,CHENG W,et al.Learning deep network representations with adversarially regularized autoencoders[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York:ACM,2018:2663-2671.
[16]GOYAL P,KAMRA N,HE X,et al.DynGEM:Deep embedding method for dynamic graphs[J].arXiv:1805.11273,2018.
[17]ZHANG C X,SONG D J,HUANG C,et al.Heterogeneousgraph neural network[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York:ACM,2019:793-803.
[18]PARK C,KIM D,HAN J,et al.Unsupervised attributed multiplex network embedding[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence.Palo Alto,CA:AAAI,2020:5371-5378.
[19]FENG Y F,YOU H X,ZHANG Z Z,et al.Hypergraph neural networks[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence.Palo Alto,CA:AAAI,2019:3558-3565.
[20]XU M J.Understanding graph embedding methods and their applications[J].SIAM Review,2021,63(4):825-853.
[21]GOYAL P,FERRARA E.Graph embedding techniques,applications,and performance:A survey[J].Knowledge-Based Systems,2018,151:78-94.
[22]YUE X,WANG Z,HUANG J G,et al.Graph embedding onbiomedical networks:Methods,applications and evaluations[J].Bioinformatics,2020,36(4):1241-1251.
[23]WU Y Z,CHEN Y K,YIN Z S,et al.A survey on graph embedding techniques for biomedical data:Methods and applications[J].Information Fusion,2023,100:101909.
[24]ANTELMI A,CORDASCO G,POLATO M,et al.A survey on hypergraph representation learning[J].ACM Computing Surveys,2023,56(1):1-38.
[25]WANG X,BO D Y,SHI C,et al.A survey on heterogeneousgraph embedding:Methods,techniques,applications and sources[J].IEEE Transactions on Big Data,2022,9(2):415-436.
[26]BELKIN M,NIYOGI P.Laplacian eigenmaps and spectral techniques for embedding and clustering[J].Advances in Neural Information Processing Systems,2001,14:585-591.
[27]MENG H,ZHANG H,DING Y,et al.Component preserving laplacian eigenmaps for data reconstruction and dimensionality reduction[J].Applied Intelligence,2023,53(23):28570-28591.
[28]CAI M,SHEN X,ABHADIOMHEN S E,et al.Robust dimensionality reduction via low-rank Laplacian graph learning[J].ACM Transactions on Intelligent Systems and Technology,2023,14(3):1-24.
[29]AHMED A,SHERVASHIDZE N,NARAYANAMURTHY S,et al.Distributed large-scale natural graph factorization[C]//Proceedings of the 22nd International Conference on World Wide Web.New York:ACM,2013:37-48.
[30]QIU J Z,DONG Y X,MA H,et al.NetSMF:Large-scale network embedding as sparse matrix factorization[C]//Proceedings of the World Wide Web Conference.New York:ACM,2019:1509-1520.
[31]AGIBETOV A.Neural graph embeddings as explicit low-rankmatrix factorization for link prediction[J].Pattern Recognition,2023,133:108977.
[32]WAN L,FU Z,LING Y,et al.Z-Laplacianmatrix factorization:Network embedding with interpretable graph signals[J].IEEE Transactions on Knowledge and Data Engineering,2023,36(8):4331-4345.
[33]GOLDBERG Y.Word2vec Explained:Deriving Mikolov et al.’s negative-sampling word-embedding method[J].arXiv:1402.3722,2014.
[34]RIBEIRO L F R,SAVERESE P H P,FIGUEIREDO D R.Struc2vec:Learning node representations from structural identity[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York:ACM,2017:385-394.
[35]RAHMAN T,SURMA B,BACKES M,et al.Fairwalk:To-wards fair graph embedding[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence.San Francisco,CA:Morgan Kaufmann,2019:3289-3295.
[36]ZHANG Y,SHEN J,ZHANG R,et al.Network representation learning via improved random walk with restart[J].Knowledge-Based Systems,2023,263:110255.
[37]WANG R,LI Y K,LIN S,et al.Common neighbors matter:Fastrandom walk sampling with common neighbor awareness[J].IEEE Transactions on Knowledge and Data Engineering,2022,35(5):4570-4584.
[38]GAWLIKOWSKI J,TASSI C R N,ALI M,et al.A survey of uncertainty in deep neural networks[J].Artificial Intelligence Review,2023,56:1513-1589.
[39]CONG S,ZHOU Y.A review of convolutional neural network architectures and their optimizations[J].Artificial Intelligence Review,2023,56(3):1905-1969.
[40]REN H T,LU W,XIAO Y,et al.Graph convolutional networks in language and vision:A survey[J].Knowledge-Based Systems,2022,251:109250.
[41]DEFFERRARD M,BRESSON X,VANDERGHEYNST P.Convolutional neural networks on graphs with fast localized spectral filtering[J].Advances in Neural Information Processing Systems,2016,29:3837-3845.
[42]KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[J].arXiv:1609.02907,2016.
[43]WU F,AMAURI H,SOUZA J,et al.Simplifying graph convolutional networks[C]//Proceedings of the 36th International Conference on Machine Learning.New York:PMLR,2019:6861-6871.
[44]JIANG X D,ZHU R H,LI P S,et al.Co-embedding of nodes and edges with graph neural networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,45(6):7075-7086.
[45]WANG W,ZHANG G W,HAN H Y,et al.Correntropy-induced Wasserstein GCN:Learning graph embedding via domain adaptation[J].IEEE Transactions on Image Processing,2023:32:3980-3993.
[46]NIEPERT M,AHMED M,KUTZKOV K.Learning convolu-tional neural networks for graphs[C]//Proceedings of the 33rd International Conference on Machine Learning.New York:PMLR,2016:2014-2023.
[47]HAMILTON W,YING Z,LESKOVEC J.Inductive representation learning on large graphs[C]//Advances in Neural Information Processing Systems.2017:1024-1034.
[48]HU F Y,ZHU Y Q,WU S,et al.GraphAIR:Graph representation learning with neighborhood aggregation and interaction[J].Pattern Recognition,2021,112:107745.
[49]TAILOR S A,OPOLKA F L,LIO P,et al.Do we need anisotropic graph neural networks?[J].arXiv:2104.01481,2021.
[50]ZHOU Y,HUO H,HOU Z,et al.Co-embedding of edges andnodes with deep graph convolutional neural networks[J].Scientific Reports,2023,13(1):16966.
[51]VELIČKOVIĆ P,CUCURULL G,CASANOVA A,et al.Graph attention networks [J].arXiv:1710.10903,2017.
[52]HE J Y,WANG J M,YU Z Z.Attention based adversariallyregularized learning for network embedding[J].Data Mining and Knowledge Discovery,2021,35(5):2112-2140.
[53]YE Y,JI S H.Sparse graph attention networks[J].IEEE Transactions on Knowledge and Data Engineering,2021,35(1):905-916.
[54]HE L C,BAI L,YANG X,et al.High-order graph attention network[J].Information Sciences,2023,630:222-234.
[55]XIE Y,ZHANG Y Q,GONG M G,et al.MGAT:Multi-viewgraph attention networks[J].Neural Networks,2020,132:180-189.
[56]HUANG Z M,REN Y Z,PU X,et al.Self-supervised graph attention networks for deep weighted multi-view clustering[C]//Proceedings of the 37th AAAI Conference on Artificial Intelligence.Palo Alto,CA:AAAI,2023:7936-7943.
[57]WANG X,ZHU M Q,BO D Y,et al.AM-GCN:Adaptive multi-channel graph convolutional networks[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York:ACM,2020:1243-1253.
[58]RUMELHART D E,HINTON G E,WILLIAMS R J.Learning representations by back-propagating errors[J].Nature,1986,323(6088):533-536.
[59]WANG D X,CUI P,ZHU W W.Structural deep network embedding[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York:ACM,2016:1225-1234.
[60]LI J,LU G Q,WU Z T,et al.Multi-view representation model based on graph autoencoder[J].Information Sciences,2023,632:439-453.
[61]SUN D D,LI D S,DING Z L,et al.Dual-decoder graph autoen-coder for unsupervised graph representation learning[J].Knowledge-Based Systems,2021,234:107564.
[62]CHEN Z L,WU Z H,WANG S P,et al.Dual low-rank graphautoencoder for semantic and topological networks[C]//Proceedings of the 37th AAAI Conference on Artificial Intelligence.Palo Alto,CA:AAAI,2023:4191-4198.
[63]KIPF T N,WELLING M.Variational graph auto-encoders[J].arXiv:1611.07308,2016.
[64]GUO L,DAI Q.Graph clustering via variational graph embedding[J].Pattern Recognition,2022,122:108334.
[65]ZHANG R,ZHANG Y X,LU C J,et al.Unsupervised graph embedding via adaptive graph learning[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2022,45(4):5329-5336.
[66]GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Ge-nerative adversarial nets[C]//Advances in Neural Information Processing Systems.2014:2672-2680.
[67]WANG H W,WANG J,WANG J L,et al.GraphGAN:Graph representation learning with generative adversarial nets[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence.Palo Alto,CA:AAAI,2018:2508-2515.
[68]HONG H T,LI X,WANG M Z.GANE:A generative adversarial network embedding[J].IEEE Transactions on Neural Networks and Learning Systems,2019,31(7):2325-2335.
[69]HE D X,WANG T,ZHAI L,et al.Adversarial representation mechanism learning for network embedding[J].IEEE Transactions on Knowledge and Data Engineering,2021,35(2):1200-1213.
[70]PAREJA A,DOMENICONI G,CHEN J,et al.EvolveGCN:Evolving graph convolutional networks for dynamic graphs[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence.Palo Alto,CA:AAAI,2020:5363-5370.
[71]GAO C,ZHU J Y,ZHANG F,et al.A novel representationlearning for dynamic graphs based on graph convolutional networks[J].IEEE Transactions on Cybernetics,2022,53(6):3599-3612.
[72]ZHANG C Y,YAO Z L,YAO H Y,et al.Dynamic representation learning via recurrent graph neural networks[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2022,53(2):1284-1297.
[73]ZHU Y F,CONG F P,ZHANG D,et al.WinGNN:Dynamic graph neural networks with random gradient aggregation window[C]//Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.New York:ACM,2023:3650-3662.
[74]TRIVEDI R,FARAJTABAR M,BISWAL P,et al.DyRep:Learning representations over dynamic graphs[C]//Procee-dings of the International Conference on Learning Representations.2024.
[75]ROSSI E,CHAMBERLAIN B,FRASCA F,et al.Temporalgraph networks for deep learning on dynamic graphs[J].arXiv:2006.10637,2020.
[76]SOUZA A,MESQUITA D,KASKI S,et al.Provably expressive temporal graph networks[J].Advances in Neural Information Processing Systems,2022,35:32257-32269.
[77]FU T Y,LEE W C,LEI Z.HIN2Vec:Explore meta-paths inheterogeneous information networks for representation learning[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.New York:ACM,2017:1797-1806.
[78]LEE S,PARK C,YU H.BHIN2vec:Balancing the type of relation in heterogeneous information network[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management.New York:ACM,2019:619-628.
[79]LIU Z,ZHANG S,ZHANG J,et al.HeteEdgeWalk:A heterogeneous edge memory random walk for heterogeneous information network embedding[J].Entropy,2023,25(7):998.
[80]WANG X,JI H Y,SHI C,et al.Heterogeneous graph attention network[C]//Proceedings of the 19th World Wide Web Confe-rence.New York:ACM,2019:2022-2032.
[81]FU X Y,ZHANG J N,MENG Z Q,et al.MAGNN:Metapath aggregated graph neural network for heterogeneous graph embedding[C]//Proceedings of the Web Conference 2020.New York:ACM,2020:2331-2341.
[82]HE Y F,YAN D C,ZHANG Y W,et al.Semantic tradeoff for heterogeneous graph embedding[J].IEEE Transactions on Computational Social Systems,2022,10(3):1263-1276.
[83]YANG X C,YAN M Y,PAN S R,et al.Simple and efficientheterogeneous graph neural network[C]//Proceedings of the 37th AAAI Conference on Artificial Intelligence.Palo Alto,CA:AAAI,2023:10816-10824.
[84]LIU W Y,CHEN P Y,YEUNG S L,et al.Principled multilayer network embedding[C]//2017 IEEE International Conference on Data Mining Workshops(ICDMW).IEEE,2017:134-141.
[85]JING B Y,PARK C Y,TONG H H.HDMI:High-order deepmultiplex infomax[C]//Proceedings of the Web Conference 2021.New York:ACM,2021:2414-2424.
[86]MO Y J,CHEN Y H,LEI Y J,et al.Multiplex graph representation learning via dual correlation reduction[J].IEEE Transactions on Knowledge and Data Engineering,2023,35(12):12814-12827.
[87]MO Y J,LEI Y J,SHEN J L,et al.Disentangled multiplexgraph representation learning[C]//Proceedings of the 40th International Conference on Machine Learning.New York:PMLR,2023:24983-25005.
[88]JI S Y,FENG Y F,JI R R,et al.Dual channel hypergraph collaborative filtering[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mi-ning.New York:ACM,2020:2020-2029.
[89]YANG C Q,WANG R J,YAO S C,et al.Semi-supervised hypergraph node classification on hypergraph line expansion[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management.New York:ACM,2022:2352-2361.
[90]ARYA D,GUPTA D K,RUDINAC S,et al.HyperSAGE:Ge-neralizing inductive representation learning on hypergraphs[J].arXiv:2010.04558,2020.
[91]ZHANG R C,ZOU Y S,MA J.Hyper-SAGNN:A self-attention based graph neural network for hypergraphs[J].arXiv:1911.02613,2019.
[92]BAI S,ZHANG F H,TORR P H S.Hypergraph convolutionand hypergraph attention[J].Pattern Recognition,2021,110:107637.
[93]WU L C,WANG D L,SONG K S,et al.Dual-view hypergraph neural networks for attributed graph learning[J].Knowledge-Based Systems,2021,227:107185.
[94]CHE X J,SUN Y P.Graph node classification algorithm based on similarity random walk aggregation[J].Journal of Jilin University(Engineering and Technology Edition),2025,55(6):2069-2075.
[95]WANG C P,WANG C K,WANG Z,et al.Edge2vec:Edge-based social network embedding[J].ACM Transactions on Knowledge Discovery from Data,2020,14(4):1-24.
[96]ZHANG Y F,GAO S Q,PEI J,et al.Improving social network embedding via new second-order continuous graph neural networks[C]//Proceedings of the 28th ACM SIGKDDInternatio-nal Conference on Knowledge Discovery & Data Mining.New York:ACM,2022:2515-2523.
[97]KUMAR S,MALLIK A,KHETARPAL A,et al.Influencemaximization in social networks using graph embedding and graph neural network[J].Information Sciences,2022,607:1617-1636.
[98]LI J K,WANG R J,ZHANG F L,et al.Attribute Heterogeneous Network Embedding Method Combining Attention Mechanisms[J].Journal of Chinese Computer Systems,2024,45(6):1466-1473.
[99]SANG L,WANG Y,ZHANG Y,et al.Intent-guided Heterogeneous Graph Contrastive Learning for Recommendation[J].IEEE Transactions on Knowledge and Data Engineering,2025,37(4):1915-1929.
[100]YU J L,YIN H Z,LI J D,et al.Self-supervised multi-channel hypergraph convolutional network for social recommendation[C]//Proceedings of the Web Conference 2021.2021:413-424.
[101]ZHANG Y,TIAN J,SUN J,et al.HKGAT:Heterogeneousknowledge graph attention network for explainable recommendation system[J].Applied Intelligence,2025,55(6):549.
[102]LI N,YANG Z,WANG J,et al.Drug-target interaction prediction using knowledge graph embedding[J].Iscience,2024,27(6):109393.
[103]BARANWAL M,MAGNER A,SALDINGER J,et al.StructGraph:A graph attention network for structure based predictions of protein-protein interactions[J].BMC Bioinformatics,2022,23(1):370.
[104]SU X R,HU L,YOU Z H,et al.Multi-view heterogeneous molecular network representation learning for protein-protein interaction prediction[J].BMC Bioinformatics,2022,23(1):234.
[105]GARCIA V,BRUNA J.Few-shot learning with graph neural networks[J].arXiv:1711.04043,2017.
[106]XIE J,MENG Y,ZHAO Y,et al.Dynamicsemantic-based spatial-temporal graph convolution network for skeleton-based human action recognition[J].IEEE Transactions on Image Processing,2024,33:6691-6704.
[107]AHMAD T,RIZVI S T H,KANWAL N.Transforming spatio-temporal self-attention using action embedding for skeleton-based action recognition[J].Journal of Visual Communication and Image Representation,2023,95:103892.
[108]YAO L,MAO C S,LUO Y.Graph convolutional networks for text classification[C]//Proceedings of the 33th AAAI Confe-rence on Artificial Intelligence.Palo Alto,CA:AAAI,2019:7370-7377.
[109]RAGESH R,SELLAMANICKAM S,IYER A,et al.HeteGCN:Heterogeneous graph convolutional networks for text classification[C]//Proceedings of the 14th ACM InternationalConfe-rence on Web Search and Data Mining.New York:ACM,2021:860-868.
[110]YANG T C,HU L M,SHI C,et al.HGAT:Heterogeneous graph attention networks for semi-supervised short text classification[J].ACM Transactions on Information Systems,2021,39(3):1-29.
[111]MA Y L,LIU X F,ZHAO L J,et al.Hybrid embedding-based text representation for hierarchical multi-label text classification[J].Expert Systems with Applications,2022,187:115905.
[112]SCHLICHTKRULL M,KIPF T N,BLOEM P,et al.Modeling relational data with graph convolutional networks[C]//The Semantic Web:15th International Conference.2018:593-607.
[113]BI Z,CHENG S,CHEN J,et al.Relphormer:Relational graph Transformer for knowledge graph representations[J].Neurocomputing,2024,566:127044.
[114]LIANG S,ZHU A,ZHANG J,et al.Hyper-node relationalgraph attention network for multi-modal knowledge graph completion[J].ACM Transactions on Multimedia Computing,Communications and Applications,2023,19(2):1-21.
[115]CHIANG W L,LIU X Q,SI S,et al.Cluster-GCN:An efficient algorithm for training deep and large graph convolutional networks[C]//Proceedings of the 25th ACM SIGKDD Internatio-nal Conference on Knowledge Discovery & Data Mining.New York:ACM,2019:257-266.
[116]LIANG J,GURUKAR S,PARTHASARATHY S.Mile:Amulti-level framework for scalable graph embedding[C]//Proceedings of the International AAAI Conference on Web and Social Media.Palo Alto,CA:AAAI,2021:361-372.
[117]JI Y G,YIN M Y,YANG H X,et al.Accelerating large-scale heterogeneous interaction graph embedding learning via importance sampling[J].ACM Transactions on Knowledge Discovery from Data,2020,15(1):1-23.
[118]FENG A S,YOU C Y,WANG S Q,et al.KerGNNs:Interpretable graph neural networks with graph kernels[C]//Proceedings of the 36th AAAI Conference on Artificial Intelligence.Palo Alto,CA:AAAI,2022:6614-6622.
[119]YANG Y M,GUAN Z Y,LI J X,et al.Interpretable and efficient heterogeneous graph convolutional network[J].IEEE Transactions on Knowledge and Data Engineering,2021,35(2):1637-1650.
[120]HUANG Q,YAMADA M,TIAN Y,et al.GraphLIME:Local interpretable model explanations for graph neural networks[J].IEEE Transactions on Knowledge and Data Engineering,2022,35(7):6968-6972.
[121]KIOUCHE A,LAGRAA S,AMROUCHE K,et al.A simplegraph embedding for anomaly detection in a stream of heterogeneous labeled graphs[J].Pattern Recognition,2021,112:107746.
[122]DUAN D,ZHANG C,TONG L,et al.An anomaly aware network embedding framework for unsupervised anomalous link detection[J].Data Mining and Knowledge Discovery,2024,38(2):501-534.
[123]HAN S,WOO S.Learning sparse latent graph representationsfor anomaly detection in multivariate time series[C]//Procee-dings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.New York:ACM,2022:2977-2986.
[124]XIE H,ZHENG D,MA J,et al.Graph-aware language modelpre-training on a large graph corpus can help multiple graph applications[C]//Proceedings of the 29th ACM SIGKDD Confe-rence on Knowledge Discovery & Data Mining.New York:ACM,2023:5270-5281.
[125]LI P Y,WANG J,QIAO Y X,et al.An effective self-supervised framework for learning expressive molecular global representations to drug discovery[J].Briefings in Bioinformatics,2021,22(6):bbab109.
[126]RONG Y,BIAN Y T,XU T Y,et al.Self-supervised graphTransformer on large-scale molecular data[J].Advances in Neural Information Processing Systems,2020,33:12559-12571.
[127]SUN X G,YIN H Z,LIU B,et al.Heterogeneous hypergraph embedding for graph classification[C]//Proceedings of the 14th ACM International Conference on Web Search and Data Mi-ning.New York:ACM,2021:725-733.
[128]LIU J,SONG L Y,WANG G T,et al.Meta-HGT:Metapath-aware hypergraph Transformer for heterogeneous information network embedding[J].Neural Networks,2023,157:65-76.
[129]FU C,YU P,YU Y,et al.MHGCN+:Multiplex heterogeneous graph convolutional network[J].ACM Transactions on Intelligent Systems and Technology,2024,15(3):1-25.

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