图表示学习综述

邹然; 柳杨; 李聪; 张怡娇; 胡延庆

doi:10.12202/j.0476-0301.2023172

图表示学习综述

doi: 10.12202/j.0476-0301.2023172

邹然¹,
柳杨¹,
李聪²,
张怡娇¹,
胡延庆^1, ,

1.
南方科技大学理学院统计与数据科学系，518055，广东深圳
2.
中山大学系统科学与工程学院，510006，广东广州

基金项目: 国家自然科学基金资助项目（12275118）

详细信息

通讯作者:
胡延庆（1980—），男，副教授，博士，博导. 研究方向：大数据、复杂网络. E-mail：huyq@sustech.edu.cn

中图分类号: TP18
计量
- 文章访问数: 64
- HTML全文浏览量: 32
- PDF下载量: 13
- 被引次数: 0
出版历程
- 收稿日期: 2023-05-21
- 刊出日期: 2023-10-31

Graph representation learning: a review

1.
Department of Statistics and Data Science，College of Science，Southern University of Science and Technology，518055，Shenzhen，Guangdong，China
2.
School of Systems Science and Engineering，Sun Yat-sen University，510006，Guangzhou，Guangdong，China

摘要

摘要: 鉴于图表示学习是一种将图结构转化为向量表示的方法，探讨了其在社交网络、生物网络、贸易网络和计算机网络等各个领域的应用情况；为了梳理图表示学习的发展历程，用全面的视角了解不同方法以及相关应用，综述了图嵌入和图神经网络这2类重要的图表示学习的研究进展；详细介绍了其中几个经典的算法；重点阐述了图表示学习在生化医疗领域的应用；深入讨论了图表示学习领域所面临的难点和挑战以及未来可能的研究方向．
- 图表示学习 /
- 图嵌入 /
- 图神经网络 /
- 复杂网络
Abstract: As a method that converts graph structure into vector representations, graph representation learning has gained significant attention in recent years in various fields such as social networks, biological networks, world trade webs, and computer networks． To document the development of graph representation learning, and to provide a comprehensive overview of different methods and their related applications, the present work summarizes progress in two important categories of graph representation learning: graph embedding and graph neural networks． A detailed overview of several classical algorithms is presented． Application of graph representation learning is introduced in biology, chemistry, and medicine． Challenges and future directions that graph representation learning faces are discussed．
- graph representation learning /
- graph embedding /
- graph neural networks /
- complex networks

HTML全文

图 1 GraphSAGE中的采样和聚合示意

下载: 全尺寸图片幻灯片

图 2 图注意力机制下的节点特征表示的更新

下载: 全尺寸图片幻灯片

参考文献(51)

[1]	PEROZZI B,AL-RFOU R,SKIENA S. DeepWalk:online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining,August 24-27,2014,New York,New York,USA. New York:ACM,2014:701
[2]	GROVER A,LESKOVEC J. Node2Vec:scalable feature learning for networks[J]. KDD:Proceedings International Conference on Knowledge Discovery & Data Mining,2016,2016:855
[3]	TANG J,QU M,WANG M Z,et al. LINE:large-scale information network embedding[C]//Proceedings of the 24th International Conference on World Wide Web. New York:ACM,2015:1067
[4]	KIPF T N,WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL].(2017-04-22)[2023-02-11]. https://arxiv.org/abs/1609.02907
[5]	HAMILTON W L,YING R,LESKOVEC J. Inductive representation learning on large graphs [EB/OL]. (2017-12-04)[2023-02-11]. https://dl.acm.org/doi/10.5555/3294771.3294869
[6]	VELIČKOVIĆ P,CUCURULL G,CASANOVA A,et al. Graph attention networks[EB/OL]. (2018-02-04)[2023-02-12].https://arxiv.org/abs/1710.10903
[7]	CUI P,WANG X,PEI J,et al. A survey on network embedding[J]. IEEE Transactions on Knowledge and Data Engineering,2019,31(5):833 doi: 10.1109/TKDE.2018.2849727
[8]	ZHANG D K,YIN J,ZHU X Q,et al. Network representation learning:a survey[J]. IEEE Transactions on Big Data,2020,6(1):3 doi: 10.1109/TBDATA.2018.2850013
[9]	CAI H Y,ZHENG V W,CHANG K C C. A comprehensive survey of graph embedding:problems,techniques,and applications[J]. IEEE Transactions on Knowledge and Data Engineering,2018,30(9):1616 doi: 10.1109/TKDE.2018.2807452
[10]	GOYAL P,FERRARA E. Graph embedding techniques,applications,and performance:a survey[J]. Knowledge-Based Systems,2018,151:78 doi: 10.1016/j.knosys.2018.03.022
[11]	PAN S R,WU J,ZHU X Q,et al. Tri-party deep network representation[EB/OL]. (2010-09-05)[2023-02-11]. https://openreview.net/forum?id=rkbOXHzdbS
[12]	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,October 18 - 23,2015,Melbourne,Australia. New York:ACM,2015:891
[13]	SHEN X B,PAN S R,LIU W W,et al. Discrete network embedding[C]//Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence,July 13-19,2018,Stockholm,Sweden. California:International Joint Conferences on Artificial Intelligence Organization,2018:3549
[14]	YANG H,PAN S R,ZHANG P,et al. Binarized attributed network embedding[C]//2018 IEEE International Conference on Data Mining (ICDM),November 17-20,2018. Singapore:IEEE,2019:1476
[15]	WANG D X,CUI P,ZHU W W. Structural deep network embedding[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,August 13-17,2016,San Francisco,California,USA. New York:ACM,2016:1225
[16]	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. New York:ACM,2016:1145
[17]	WANG S H,TANG J L,AGGARWAL C,et al. Signed network embedding in social media[C]//Proceedings of the 2017 SIAM International Conference on Data Mining. Philadelphia,PA:Society for Industrial and Applied Mathematics,2017:327
[18]	BRUNA J,ZAREMBA W,SZLAM A,et al. Spectral networks and locally connected networks on graphs[EB/OL]. (2014-05-21)[2023-02-11]. https://arxiv.org/abs/1312.6203
[19]	DEFFERRARD M,BRESSON X,VANDERGHEYNST P. Convolutional neural networks on graphs with fast localized spectral filtering[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. New York:ACM,2016:3844
[20]	LI R Y,WANG S,ZHU F Y,et al. Adaptive graph convolutional neural networks[EB/OL]. (2018-04-29)[2023-02-12]. https://ojs.aaai.org/index.php/AAAI/article/view/11691
[21]	LEVIE R,MONTI F,BRESSON X,et al. CayleyNets:graph convolutional neural networks with complex rational spectral filters[J]. IEEE Transactions on Signal Processing,2019,67(1):97 doi: 10.1109/TSP.2018.2879624
[22]	SCARSELLI F,GORI M,TSOI A C,et al. The graph neural network model[J]. IEEE Transactions on Neural Networks,2009,20(1):61 doi: 10.1109/TNN.2008.2005605
[23]	GALLICCHIO C,MICHELI A. Graph echo state networks[C]//The 2010 International Joint Conference on Neural Networks (IJCNN),July 18-23,2010. Barcelona,Spain:IEEE,2010:1
[24]	LI Y J,TARLOW D,BROCKSCHMIDT M,et al. Gated graph sequence neural networks[EB/OL]. (2017-09-22)[2023-02-12]. https://arxiv.org/abs/1511.05493
[25]	SCHLICHTKRULL M,KIPF T N,BLOEM P,et al. Modeling relational data with graph convolutional networks[C]//European Semantic Web Conference. Cham:Springer,2018:593
[26]	ZHANG C X,SONG D J,HUANG C,et al. Heterogeneous graph neural network[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,August 4-8,2019,Anchorage,AK,USA. New York:ACM,2019:793
[27]	WANG X,JI H Y,SHI C,et al. Heterogeneous graph attention network[C]//WWW '19:The World Wide Web Conference,May 13 - 17,2019,San Francisco,CA,USA. New York:ACM,2019:2022
[28]	YUN S,JEONG M,KIM R,et al. Graph transformer networks[EB/OL]. (2020-02-05)[2023-02-12]. https://arxiv.org/abs/1911.06455
[29]	ZHU S C,ZHOU C,PAN S R,et al. Relation structure-aware heterogeneous graph neural network[C]//2019 IEEE International Conference on Data Mining (ICDM),November 8-11,2019. Beijing,China:IEEE,2020:1534
[30]	FU X Y,ZHANG J N,MENG Z Q,et al. MAGNN:metapath aggregated graph neural network for heterogeneous graph embedding[EB/OL]. (2020-04-20)[2023-02-12]. https://dl.acm.org/doi/abs/10.1145/3366423.3380297
[31]	HU Z N,DONG Y X,WANG K S,et al. Heterogeneous graph transformer[EB/OL]. (2020-04-20)[2023-02-12]. https://dl.acm.org/doi/abs/10.1145/3366423.3380027
[32]	HONG H T,GUO H T,LIN Y C,et al. An attention-based graph neural network for heterogeneous structural learning[J]. Proceedings of the AAAI Conference on Artificial Intelligence,2020,34(4):4132 doi: 10.1609/aaai.v34i04.5833
[33]	BATTAGLIA P,PASCANU R,LAI M,et al. Interaction networks for learning about objects,relations and physics[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems,December 5-10,2016,Barcelona,Spain. New York:ACM,2016:4509
[34]	CHANG M B,ULLMAN T,TORRALBA A,et al. A compositional object-based approach to learning physical dynamics[EB/OL]. (2017-03-04)[2023-02-12]. https://arxiv.org/abs/1612.00341
[35]	GILMER J,SCHOENHOLZ S S,RILEY P F,et al. Neural message passing for Quantum chemistry[C]//Proceedings of the 34th International Conference on Machine Learning - Volume 70,August 6 - 11,2017,Sydney,NSW,Australia. New York:ACM,2017:1263
[36]	DUVENAUD D,MACLAURIN D,AGUILERA-IPARRAGUIRRE J,et al. Convolutional networks on graphs for learning molecular fingerprints[EB/OL]. (2015-11-03)[2023-02-12]. https://arxiv.org/abs/1509.09292
[37]	YUAN S J,WANG C,JIANG Q,et al. Community detection with graph neural network using Markov stability[EB/OL]. (2022-03-01)[2023-02-12]. https://ieeexplore.ieee.org/document/9722614
[38]	HUANG K X,XIAO C,GLASS L M,et al. SkipGNN:predicting molecular interactions with skip-graph networks[J]. Scientific Reports,2020,10:21092 doi: 10.1038/s41598-020-77766-9
[39]	LI Y,QIAO G Y,GAO X,et al. Supervised graph co-contrastive learning for drug-target interaction prediction[J]. Bioinformatics,2022,38(10):2847 doi: 10.1093/bioinformatics/btac164
[40]	LI M,CAI X R,LI L Y,et al. Heterogeneous graph attention network for drug-target interaction prediction[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management,October 17-21,2022,Atlanta,GA,USA. New York:ACM,2022:1166
[41]	BELKIN M,NIYOGI P,SINDHWANI V. Manifold regularization:a geometric framework for learning from labeled and unlabeled examples[J]. Journal of Machine Learning Research,2006,7:2399
[42]	WESTON J,RATLE F,COLLOBERT R. Deep learning via semi-supervised embedding[EB/OL]. (2008-07-05)[2023-02-12]. https://dl.acm.org/doi/abs/10.1145/1390156.1390303
[43]	ZHU X J,GHAHRAMANI Z,LAFFERTY J. Semi-supervised learning using Gaussian fields and harmonic functions[C]//Proceedings of the Twentieth International Conference on International Conference on Machine Learning,August 21 - 24,2003,Washington,DC,USA. New York:ACM,2003:912
[44]	LU Q,GETOOR L. Link-based classification[EB/OL]. (2003-08-21)[2023-02-12]. https://dl.acm.org/doi/10.5555/3041838.3041901
[45]	YANG Z L,COHEN W W,SALAKHUTDINOV R. Revisiting semi-supervised learning with graph embeddings[C]//Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48. New York:ACM,2016:40
[46]	马腾飞. 图神经网络基础与前沿[M]. 北京:电子工业出版社,2021
[47]	JIN W G,COLEY C W,BARZILAY R,et al. Predicting organic reaction outcomes with Weisfeiler-Lehman network[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. New York:ACM,2017:2604
[48]	LIU C H,KORABLYOV M,JASTRZĘBSKI S,et al. RetroGNN:fast estimation of synthesizability for virtual screening and de novo design by learning from slow retrosynthesis software[J]. Journal of Chemical Information and Modeling,2022,62(10):2293 doi: 10.1021/acs.jcim.1c01476
[49]	LI G H,LUO J W,XIAO Q,et al. Predicting microRNA-disease associations using network topological similarity based on DeepWalk[J]. IEEE Access,2017,5:24032 doi: 10.1109/ACCESS.2017.2766758
[50]	TORNG W,ALTMAN R B. Graph convolutional neural networks for predicting drug-target interactions[J]. Journal of Chemical Information and Modeling,2019,59(10):4131 doi: 10.1021/acs.jcim.9b00628
[51]	LI S L,ZHOU J B,XU T,et al. Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining,August 14 - 18,2021,Virtual Event,Singapore. New York:ACM,2021:975