论文解读目录

目录

01、论文解读（LINE）《LINE: Large-scale Information Network Embedding》——2015, WWW

02、论文解读（GraphSAGE）《Inductive Representation Learning on Large Graphs》——2017, NIPS

03、论文解读（DFCN）《Deep Fusion Clustering Network》——2020, AAAI——2022-01-27

04、论文解读（GALA）《Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning》——2020, AAAI

05、论文解读（DEC）《Unsupervised Deep Embedding for Clustering Analysis》——2016,ICML

06、论文解读（SDCN）《Structural Deep Clustering Network》——2020, WWW

07、论文解读（SDNE）《Structural Deep Network Embedding》——2016, KDD

08、论文解读（IDEC）《Improved Deep Embedded Clustering with Local Structure Preservation》 ——2017, IJCAI

09、论文解读（AGCN）《 Attention-driven Graph Clustering Network》——2021, ACM Multimedia——2022-02-17

10、论文解读（DAEGC）《Attributed Graph Clustering: A Deep Attentional Embedding Approach》——2019, IJCAI

12、论文解读（Geom-GCN）《Geom-GCN: Geometric Graph Convolutional Networks》——2020, ICLR

13、论文解读（GIN）《How Powerful are Graph Neural Networks》——2019, ICLR

14、论文解读（GraphCL）《Graph Contrastive Learning with Augmentations》——2020, NeurIPS

15、论文解读（VGAE）《Variational Graph Auto-Encoders》——2016, ArXiv

16、论文解读（SUGRL）《Simple Unsupervised Graph Representation Learning》——2022 AAAI

17、论文解读（DGI）《Deep Graph Infomax》——2019,ICLR 

18、论文解读（MVGRL）《Contrastive Multi-View Representation Learning on Graphs》  ——2020, ICML

19、论文解读（GRACE）《Deep Graph Contrastive Representation Learning》 ——2020, ArXiv

20、论文解读（Graph-MLP）《Graph-MLP: Node Classification without Message Passing in Graph》——2021, ArXiv

21、论文解读（SupCosine）《Supervised Contrastive Learning with Structure Inference for Graph Classification》——2022, ArXiv

22、论文解读（ N2N）《Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization》——2022, CVPR

23、论文解读（GMI）《Graph Representation Learning via Graphical Mutual Information Maximization》——2020, WWW

24、论文解读（CSSL）《Contrastive Self-supervised Learning for Graph Classification》 —— 2020, AAAI

25、论文解读（GRCCA）《 Graph Representation Learning via Contrasting Cluster Assignments》—— 2021, ArXiv

26、论文解读（MLGCL）《Multi-Level Graph Contrastive Learning》——2021, Neurocomputing

27   论文解读（GCA）《Graph Contrastive Learning with Adaptive Augmentation》——2021, WWW

28、论文解读（BGRL）《Large-Scale Representation Learning on Graphs via Bootstrapping》——2021, ICLR

29、论文解读（SelfGNN）《Self-supervised Graph Neural Networks without explicit negative sampling》——2021, WWW

30、论文解读（Cluster-GCN）《Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks》——2019, KDD

31、论文解读（DMVCJ）《Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs》——2022, ArXiv

32、论文解读（ AF-GCL）《Augmentation-Free Graph Contrastive Learning with Performance Guarantee》——2022, ArXiv

33、论文解读（GMAE）《Graph Masked Autoencoders with Transformers》——2022, ArXiv

34、论文解读（GraphMAE）《GraphMAE: Self-Supervised Masked Graph Autoencoders》——2022, KDD

35、论文解读（MGAE）《MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs》——2022, ArXiv

36、 论文解读（GLNNs）《Graph-Less Neural Networks: Teaching Old MLPs New Tricks Via Distillation》——2022, ICLR

37、论文解读（KP-GNN）《How Powerful are K-hop Message Passing Graph Neural Networks》——2022, ArXiv

38、论文解读（USIB）《Towards Explanation for Unsupervised Graph-Level Representation Learning》——2022, ArXiv

39、论文解读（SAIL）《SAIL: Self-Augmented Graph Contrastive Learning》——2022,AAAI

40、论文解读（SCGC）《SCGC : Self-Supervised Contrastive Graph Clustering》——2022, ArXiv

41、论文解读（GCC）《Graph Contrastive Clustering》——2021, ICCV

42、论文解读（DCRN）《Deep Graph Clustering via Dual Correlation Reduction》——2022, AAAI

43、论文解读（GAT）《Graph Attention Networks》——2018, ICLR

44、论文解读（SR-GNN）《Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data》——2021,NeurIPS

45、论文解读（LG2AR）《Learning Graph Augmentations to Learn Graph Representations》——2022, ArXiv

46、论文解读（GCC）《Efficient Graph Convolution for Joint Node RepresentationLearning and Clustering》——2021, WSDM

47、论文解读（Linear GAE）《Simple and Effective Graph Autoencoders with One-Hop Linear Models》——2020, ECML/PKDD

48、 论文解读（DCN）《Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering》——2016, ICML

49、论文解读（AGC）《Attributed Graph Clustering via Adaptive Graph Convolution》——2019, IJCAI

50、论文解读（ValidUtil）《Rethinking the Setting of Semi-supervised Learning on Graphs》

51、论文解读（GCNII）《Simple and Deep Graph Convolutional Networks》——2020,PMLR

52、论文解读（MaskGAE）《MaskGAE: Masked Graph Modeling Meets Graph Autoencoders》 ——2022, ArXiv

53、论文解读（MSN）《Masked Siamese Networks for Label-Effificient Learning》——2022, ArXiv

54、论文解读（SAGA）《Siamese Attribute-missing Graph Auto-encoder》——2021, ArXiv

55、论文解读（DeepWalk）《DeepWalk: Online Learning of Social Representations》  ——2014,KDD

56、论文解读（GSAT）《Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism》——2022,ICML

57、论文解读（node2vec）《node2vec Scalable Feature Learning for Networks》——2016,KDD

58、论文解读（SAGPool）《Self-Attention Graph Pooling》——2019, ICML

59、论文解读（DiffPool）《Hierarchical Graph Representation Learning with Differentiable Pooling》 ——2018, NeurIPS

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