Computer Science ›› 2023, Vol. 50 ›› Issue (7): 207-212.doi: 10.11896/jsjkx.220500093
• Artificial Intelligence • Previous Articles Next Articles
JIANG Linpu1, CHEN Kejia1,2
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[1]CHEN J,MA T,XIAO C.FastGCN:Fast Learning with Graph Convolutional Networks via Importance Sampling[C]//Procee-dings of the 6th International Conference on Learning Representations.2018. [2]HOU H,HILDRUN K,LIU Z.The Structure of Scientific Collaboration Networks in Scientometrics [J].Scientometrics,2008,75(2):189-202. [3]LIAO R,ZHAO Z,RAQUEL U,et al.LanczosNet:Multi-Scale Deep Graph Convolutional Networks[C]//Proceedings of the 7th International Conference on Learning Representations.2019. [4]PETAR V,GUILLEM C,ARANTXA C,et al.Graph Attention Networks[C]//Proceedings of the 6th International Conference on Learning Representations.2018. [5]WU F,AMAURI H S J,ZHANG T,et al.Simplifying Graph Convolutional Networks[C]//Proceedings of the 36th International Conference on Machine Learning.2019:6861-6871. [6]THOMAS N K,MAX W.Semi-Supervised Classification withGraph Convolutional Networks[C]//Proceedings of the 5th International Conference on Learning Representations.2017. [7]QU M,YOSHUA B,TANG J.GMNN:Graph Markov Neural Networks[C]//Proceedings of the 36th International Confe-rence on Machine Learning.2019:5241-5250. [8]LIU Y,PAN S,JIN M,et al.Graph Self-supervised Learning:a Survey [J].arXiv:2103.00111,2021. [9]BRYAN P,RAMI A,STEVEN S.DeepWalk:Online Learning of Social Representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2014:701-710. [10]ADITYA G,JURE L.Node2vec:Scalable Feature Learning for Networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2016:855-864. [11]PETAR V,WILLIAM F,WILLIAM L H.Deep Graph Infomax[C]//Proceedings of the 7th International Conference on Lear-ning Representations.2019. [12]ZHU Y,XU Y,YU F,et al.Graph Contrastive Learning with Adaptive Augmentation[C]//Proceedings of the Web Confe-rence.2021:2069-2080. [13]PENG Z,HUANG W,LUO M,et al.Graph RepresentationLearning via Graphical Mutual Information Maximization[C]//Proceedings of the Web Conference.2020:259-270. [14]PETER E.Predictive Coding-I [J].IRE Transactions on Information Theory,1955,1(1):16-24. [15]BISHNU S A,MANFRED R S.Adaptive Predictive Coding of Speech Signals [J].The Bell System Technical Journal,1970,49(8):1973-1986. [16]GIANG H N,JOHN B L,RYAN A R,et al.Continuous-Time Dynamic Network Embeddings[C]//Proceedings of the Web Conference.2018:969-976. [17]ZUO Y,LIU G,LIN H,et al.Embedding Temporal Network via Neighborhood Formation[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2018:2857-2866. [18]RAKSHIT T,MEGRDAD F,PRASENJEET B,et al.DyRep:Learning Representations over Dynamic Graphs[C]//Procee-dings of the 7th International Conference on Learning Representations.2019. [19]EMANUELE R,BEN C,FABRIZIO F,et al.Temporal GraphNetworks for Deep Learning on Dynamic Graphs [J].arXiv:2006.10637,2020. [20]PALASH G,NITIN K,XINRAN H,et al.DynGEM:Deep Embedding Method for Dynamic Graphs [J].arXiv:1805.11273,2018. [21]ZHOU L K,YANG Y,REN X,et al.Dynamic Network Embedding by Modeling Triadic Closure Process[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence.2018:571-578. [22]PALASH G,SUJIT R C,ARQUIMEDES C.Dyngraph2vec:Capturing Network Dynamics Using Dynamic Graph Representation Learning [J].Knowledge Base System,2020,187:104816.1-104816.9. [23]ARAVIND S,WU Y,GOU L,et al.DySAT:Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks[C]//Proceedings of the 13th ACM International Confe-rence on Web Search and Data Mining.2020:519-527. [24]JING L,TIAN Y.Self-supervised Visual Feature Learning with Deep Neural Networks:a Survey [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2021,43(11):4037-4058. [25]LIU X,ZHANG F,HOU Z,et al.Self-supervised Learning:Generative or Contrastive[J].arXiv:2006.08218,2020. [26]AARON V D O,LI Y,ORIOLO V.Representation Learning with Contrastive Predictive Coding [J].arXiv:1807.03748,2018. [27]HJELM R D,ALEX F,SAMUEL L,et al.Learning Deep Representations by Mutual Information Estimation and Maximization[C]//Proceedings of the 7th International Conference on Learning Representations.2019. [28]ISHMAEL B,SAI R,ARISTIDE B,et al.MINE:Mutual Information Neural Estimation [J].arXiv:1801.04062,2018. [29]ZHU Y,XU Y,YU F,et al.Deep Graph Contrastive Representation Learning [J].arXiv:2006.04131,2020. [30]TIAN S,WU R,SHI L,et al.Self-supervised RepresentationLearning on Dynamic Graphs[C]//Proceedings of the 30th ACM International Conference on Information and Knowledge Management.2021:1814-1823. [31]JONAS G,MICHAEL A,DAVID G,et al.Convolutional Sequence to Sequence Learning[C]//Proceedings of the 34th International Conference on Machine Learning.2017:1243-1252. [32]ASHISH V,NOAM S,NIKI P,et al.Attention is All you Need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.2017:6000-6010. [33]YOU Y,CHEN T,WANG Z,et al.When does Self-Supervision Help Graph Convolutional Networks?[C]//Proceedings of the 37th International Conference on Machine Learning.2020:10871-10880. [34]MARTIN A,ASHISH A,PAUL B,et al.Tensorflow:Large-scale Machine Learning on Heterogeneous Distributed Systems [J].arXiv:1603.04467,2016. [35]DIEDERIK P K,JIMM Y B.Adam:A Method for Stochastic Optimization[C]//Proceedings of the 3rd International Confe-rence on Learning Representations.2015. [36]HARPER F M,JOSEPH A K.The Movielens Datasets:History and Context [J].ACM Transactions on Interactive Intelligent Systems,2016,5(4):1-19. [37]WILL H,YING Z,JURE L.Inductive Representation Learning on Large Graphs [C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.2017:1024-1034. [38]MARINKA Z,MONICA A,JURE L.Modeling Polypharmacy Side Effects with Graph Convolutional Networks [J].Bioinformatics,2018,34(13):457-466. |
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