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、
39、
40、
41、
42、
43、
44、
45、
46、
47、
48、
49、