Fetching the paper…
Reading the bibliography…
Deep learning on graphs has attracted significant interests recently.
S. Wold, K. Esbensen, and P. Geladi, “Principal component analysis,” Chemometrics and intelligent laboratory systems , 1987
1987
Earlier work this paper cites.
A. K. Debnath, R. L. Lopez de Compadre, G. Debnath, A. J. Shusterman, and C. Hansch, “Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity,” Journal of medicinal chemistry , vol. 34, no. 2, pp. 786–797, 1991
1991
Earlier work this paper cites.
G. Karypis and V. Kumar, “Multilevel graph partitioning schemes,” in ICPP , 1995, pp. 113–122
1995
Earlier work this paper cites.
G. Karypis and V. Kumar, “A fast and high quality multilevel scheme for partitioning irregular graphs,” SIAM Journal on scientific Computing , vol. 20, no. 1, pp. 359–392, 1998
1998
Earlier work this paper cites.
C. Helma, R. D. King, S. Kramer, and A. Srinivasan, “The predictive toxicology challenge 2000–2001,” Bioinformatics , 2001
2001
Earlier work this paper cites.
P. D. Dobson and A. J. Doig, “Distinguishing enzyme structures from non-enzymes without alignments,” Journal of molecular biology , vol. 330, no. 4, pp. 771–783, 2003
2003
Earlier work this paper cites.
H. Toivonen, A. Srinivasan, R. D. King, S. Kramer, and C. Helma, “Statistical evaluation of the predictive toxicology challenge 2000–2001,” Bioinformatics , vol. 19, no. 10, pp. 1183–1193, 2003
2003
Earlier work this paper cites.
K. M. Borgwardt, C. S. Ong, S. Schönauer, S. Vishwanathan, A. J. Smola, and H.-P. Kriegel, “Protein function prediction via graph kernels,” Bioinformatics , vol. 21, no. suppl_1, pp. i47–i56, 2005
2005
Earlier work this paper cites.
G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” science , vol. 313, no. 5786, pp. 504–507, 2006
2006
Earlier work this paper cites.
S. E. Schaeffer, “Graph clustering,” Computer science review , vol. 1, no. 1, pp. 27–64, 2007
2007
Earlier work this paper cites.
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, “Collective classification in network data,” AI magazine , vol. 29, no. 3, pp. 93–93, 2008
2008
Earlier work this paper cites.
N. Wale, I. A. Watson, and G. Karypis, “Comparison of descriptor spaces for chemical compound retrieval and classification,” Knowledge and Information Systems , vol. 14, no. 3, 2008
2008
Earlier work this paper cites.
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, and L. Bottou, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.” Journal of machine learning research , vol. 11, no. 12, 2010
2010
Earlier work this paper cites.
E. J. Gardiner, J. D. Holliday, C. O’Dowd, and P. Willett, “Effectiveness of 2D fingerprints for scaffold hopping,” Future medicinal chemistry , vol. 3, no. 4, pp. 405–414, 2011
2011
Earlier work this paper cites.
I. F. Martins, A. L. Teixeira, L. Pinheiro, and A. Falcao, “A Bayesian approach to in silico blood-brain barrier penetration modeling,” JCIM , vol. 52, no. 6, pp. 1686–1697, 2012
2012
Earlier work this paper cites.
T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in ICLR , 2013
2013
Earlier work this paper cites.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in NeurIPS , vol. 26, 2013, pp. 3111–3119
2013
Earlier work this paper cites.
P. A. Novick, O. F. Ortiz, J. Poelman, A. Y. Abdulhay, and V. S. Pande, “SWEETLEAD: an in silico database of approved drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery,” PloS one , vol. 8, no. 11, p. e79568, 2013
2013
Earlier work this paper cites.
B. Perozzi, R. Al-Rfou, and S. Skiena, “DeepWalk: Online learning of social representations,” in SIGKDD , 2014, pp. 701–710
2014
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in ICLR , 2014, pp. 1–14
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in NeurIPS , vol. 2. MIT Press, 2014, p. 2672–2680
2014
Earlier work this paper cites.
Tox21, “Tox21 data challenge 2014,” 2014. [Online]. Available: https://tripod.nih.gov/tox21/challenge/
2014
Earlier work this paper cites.
J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, “LINE: Large-scale information network embedding,” in WWW , 2015
2015
Earlier work this paper cites.
P. Yanardag and S. Vishwanathan, “Deep graph kernels,” in SIGKDD , 2015, pp. 1365–1374
2015
Earlier work this paper cites.
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in CVPR , 2016, pp. 2536–2544
2016
Earlier work this paper cites.
R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in ECCV . Springer, 2016, pp. 649–666
2016
Earlier work this paper cites.
M. Noroozi and P. Favaro, “Unsupervised learning of visual representations by solving jigsaw puzzles,” in ECCV . Springer, 2016, pp. 69–84
2016
Earlier work this paper cites.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in SIGKDD , 2016, pp. 855–864
2016
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Variational graph auto-encoders,” in NeurIPS Workshop , 2016, pp. 1–3
2016
Earlier work this paper cites.
A. M. Richard, R. S. Judson, K. A. Houck, C. M. Grulke, P. Volarath, I. Thillainadarajah, C. Yang, J. Rathman, M. T. Martin, J. F. Wambaugh et al. , “ToxCast chemical landscape: paving the road to 21st century toxicology,” Chemical research in toxicology , vol. 29, no. 8, pp. 1225–1251, 2016
2016
Earlier work this paper cites.
M. Kuhn, I. Letunic, L. J. Jensen, and P. Bork, “The SIDER database of drugs and side effects,” Nucleic acids research , vol. 44, no. D1, pp. D1075–D1079, 2016
2016
Earlier work this paper cites.
G. Subramanian, B. Ramsundar, V. Pande, and R. A. Denny, “Computational modeling of β \beta -secretase 1 (BACE-1) inhibitors using ligand based approaches,” JCIM , vol. 56, no. 10, pp. 1936–1949, 2016
2016
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in ICLR , 2017, pp. 1–14
2017
Earlier work this paper cites.
C. Wang, S. Pan, G. Long, X. Zhu, and J. Jiang, “MGAE: Marginalized graph autoencoder for graph clustering,” in CIKM , 2017
2017
Earlier work this paper cites.
W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in NeurIPS , 2017, p. 1025–1035
2017
Earlier work this paper cites.
M. Zitnik and J. Leskovec, “Predicting multicellular function through multi-layer tissue networks,” Bioinformatics , vol. 33, no. 14, pp. i190–i198, 2017
2017
Earlier work this paper cites.
“AIDS antiviral screen data,” 2017. [Online]. Available: http://wiki.nci.nih.gov/display/NCIDTPdata/AIDS
2017
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in AISTATS , 2017, pp. 1273–1282
2017
Earlier work this paper cites.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in ICLR , 2018, pp. 1–12
2018
Earlier work this paper cites.
Q. Liu, M. Allamanis, M. Brockschmidt, and A. L. Gaunt, “Constrained graph variational autoencoders for molecule design,” in NeurIPS , vol. 31, 2018, pp. 7806–7815
2018
Earlier work this paper cites.
S. Pan, R. Hu, G. Long, J. Jiang, L. Yao, and C. Zhang, “Adversarially regularized graph autoencoder for graph embedding,” in IJCAI , 2018, pp. 2609–2615
2018
Earlier work this paper cites.
M. Caron, P. Bojanowski, A. Joulin, and M. Douze, “Deep clustering for unsupervised learning of visual features,” in ECCV , 2018, pp. 132–149
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
R. Ying, J. You, C. Morris, X. Ren, W. L. Hamilton, and J. Leskovec, “Hierarchical graph representation learning with differentiable pooling,” in NeurIPS , 2018, p. 4805–4815
2018
Earlier work this paper cites.
O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann, “Pitfalls of graph neural network evaluation,” in NeurIPS , 2018
2018
Earlier work this paper cites.
K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” in ICLR , 2019, pp. 1–17
2019
Earlier work this paper cites.
Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph wavenet for deep spatial-temporal graph modeling,” in IJCAI , 2019
2019
Earlier work this paper cites.
P. Veličković, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, and R. D. Hjelm, “Deep graph infomax,” in ICLR , 2019, pp. 1–17
2019
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in NAACL , 2019, pp. 4171–4186
2019
Earlier work this paper cites.
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, “XLNet: Generalized autoregressive pretraining for language understanding,” in NeurIPS , vol. 32, 2019
2019
Earlier work this paper cites.
J. Park, M. Lee, H. J. Chang, K. Lee, and J. Y. Choi, “Symmetric graph convolutional autoencoder for unsupervised graph representation learning,” in ICCV , 2019, pp. 6519–6528
2019
Earlier work this paper cites.
E. Hajiramezanali, A. Hasanzadeh, N. Duffield, K. Narayanan, M. Zhou, and X. Qian, “Semi-implicit graph variational auto-encoders,” in NeurIPS , vol. 32, 2019, pp. 10 712–10 723
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
R. D. Hjelm, A. Fedorov, S. Lavoie-Marchildon, K. Grewal, P. Bachman, A. Trischler, and Y. Bengio, “Learning deep representations by mutual information estimation and maximization,” in ICLR , 2019
2019
Earlier work this paper cites.
F. L. Opolka, A. Solomon, C. Cangea, P. Veličković, P. Liò, and R. D. Hjelm, “Spatio-temporal deep graph infomax,” in ICLR Workshop , 2019, pp. 1–6
2019
Cited alongside, same era.
J. Klicpera, S. Weißenberger, and S. Günnemann, “Diffusion improves graph learning,” in NeurIPS , vol. 32, 2019
2019
Cited alongside, same era.
K. Ding, J. Li, R. Bhanushali, and H. Liu, “Deep anomaly detection on attributed networks,” in SDM . SIAM, 2019, pp. 594–602
2019
Cited alongside, same era.
Y. Li, X. Huang, J. Li, M. Du, and N. Zou, “SpecAE: Spectral autoencoder for anomaly detection in attributed networks,” in CIKM , 2019, pp. 2233–2236
2019
Cited alongside, same era.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE TNNLS , vol. 32, no. 1, pp. 4–24, 2020
2020
Cited alongside, same era.
Z. Lin, Z. Kang, L. Zhang, and L. Tian, “Multi-view attributed graph clustering,” IEEE TKDE , 2021
2021
Closest in time.
Z. Kang, Z. Lin, X. Zhu, and W. Xu, “Structured graph learning for scalable subspace clustering: From single view to multiview,” IEEE TCYB , 2021
2021
Closest in time.
X. Wang and G.-J. Qi, “Contrastive learning with stronger augmentations,” arXiv:2104.07713 , 2021
2021
Closest in time.
M. Jin, Y. Zheng, Y.-F. Li, C. Gong, C. Zhou, and S. Pan, “Multi-scale contrastive siamese networks for self-supervised graph representation learning,” in IJCAI , 2021
2021
Closest in time.
Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang, “Graph contrastive learning with adaptive augmentation,” in WWW , 2021
2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Z. Li, X. Shen, Y. Jiao, X. Pan, P. Zou, X. Meng, C. Yao, and J. Bu, “Hierarchical bipartite graph neural networks: Towards large-scale e-commerce applications,” in ICDE . IEEE, 2020
2020
Cited alongside, same era.
Z. Hu, Y. Dong, K. Wang, K.-W. Chang, and Y. Sun, “GPT-GNN: Generative pre-training of graph neural networks,” in SIGKDD , 2020, pp. 1857–1867
2020
Cited alongside, same era.
Y. Rong, Y. Bian, T. Xu, W. Xie, Y. WEI, W. Huang, and J. Huang, “Self-supervised graph transformer on large-scale molecular data,” in NeurIPS , vol. 33, 2020, pp. 12 559–12 571
2020
Cited alongside, same era.
Y. Rong, W. Huang, T. Xu, and J. Huang, “DropEdge: Towards deep graph convolutional networks on node classification,” in ICLR , 2020, pp. 1–17
2020
Cited alongside, same era.
M. Zhang, L. Hu, C. Shi, and X. Wang, “Adversarial label-flipping attack and defense for graph neural networks,” in ICDM . IEEE, 2020, pp. 791–800
2020
Cited alongside, same era.
K. Hassani and A. H. Khasahmadi, “Contrastive multi-view representation learning on graphs,” in ICML . PMLR, 2020
2020
Cited alongside, same era.
J. Qiu, Q. Chen, Y. Dong, J. Zhang, H. Yang, M. Ding, K. Wang, and J. Tang, “GCC: Graph contrastive coding for graph neural network pre-training,” in SIGKDD , 2020, pp. 1150–1160
2020
Cited alongside, same era.
Y. You, T. Chen, Y. Shen, and Z. Wang, “Graph contrastive learning automated,” in ICML . PMLR, 2021
2021
Closest in time.
H. Zhang, S. Lin, W. Liu, P. Zhou, J. Tang, X. Liang, and E. P. Xing, “Iterative graph self-distillation,” in WWW Workshop , 2021
2021
Closest in time.
J. Zeng and P. Xie, “Contrastive self-supervised learning for graph classification,” in AAAI , vol. 35, no. 12, 2021
2021
Closest in time.
S. Suresh, P. Li, C. Hao, and J. Neville, “Adversarial graph augmentation to improve graph contrastive learning,” in NeurIPS , 2021
2021
Closest in time.
S. Wan, Y. Zhan, L. Liu, B. Yu, S. Pan, and C. Gong, “Contrastive graph poisson networks: Semi-supervised learning with extremely limited labels,” in NeurIPS , 2021
2021
Closest in time.
X. Wang, N. Liu, H. Han, and C. Shi, “Self-supervised heterogeneous graph neural network with co-contrastive learning,” in SIGKDD , 2021, pp. 1726–1736
2021
Closest in time.
S. Thakoor, C. Tallec, M. G. Azar, R. Munos, P. Veličković, and M. Valko, “Bootstrapped representation learning on graphs,” in ICLR Workshop , 2021
2021
Closest in time.
Z. T. Kefato and S. Girdzijauskas, “Self-supervised graph neural networks without explicit negative sampling,” in WWW Workshop , 2021
2021
Closest in time.
J. Zbontar, L. Jing, I. Misra, Y. LeCun, and S. Deny, “Barlow twins: Self-supervised learning via redundancy reduction,” in ICML , 2021
2021
Closest in time.
2021
Closest in time.
X. Chen and K. He, “Exploring simple siamese representation learning,” in CVPR , 2021, pp. 15 750–15 758
2021
Closest in time.
V. Verma, T. Luong, K. Kawaguchi, H. Pham, and Q. Le, “Towards domain-agnostic contrastive learning,” in ICML . PMLR, 2021, pp. 10 530–10 541
2021
Closest in time.
Y. Ren, J. Bai, and J. Zhang, “Label contrastive coding based graph neural network for graph classification,” in Database Systems for Advanced Applications , 2021, pp. 123–140
2021
Closest in time.
C. Mavromatis and G. Karypis, “Graph infoclust: Maximizing coarse-grain mutual information in graphs,” in PAKDD . Springer, 2021, pp. 541–553
2021
Closest in time.
X. Li, D. Ding, B. Kao, Y. Sun, and N. Mamoulis, “Leveraging meta-path contexts for classification in heterogeneous information networks,” in ICDE . IEEE, 2021, pp. 912–923
2021
Closest in time.
Q. Zhu, Y. Xu, H. Wang, C. Zhang, J. Han, and C. Yang, “Transfer learning of graph neural networks with ego-graph information maximization,” in WWW Workshop , 2021
2021
Closest in time.
P. Wang, K. Agarwal, C. Ham, S. Choudhury, and C. K. Reddy, “Self-supervised learning of contextual embeddings for link prediction in heterogeneous networks,” in WWW , 2021
2021
Closest in time.
J. D. Robinson, C.-Y. Chuang, S. Sra, and S. Jegelka, “Contrastive learning with hard negative samples,” in ICLR , 2021, pp. 1–29
2021
Closest in time.
J. Cao, X. Lin, S. Guo, L. Liu, T. Liu, and B. Wang, “Bipartite graph embedding via mutual information maximization,” in WSDM , 2021, pp. 635–643
2021
Closest in time.
2021
Closest in time.
A. Subramonian, “Motif-driven contrastive learning of graph representations,” in AAAI , vol. 35, no. 18, 2021, pp. 15 980–15 981
2021
Closest in time.
Q. Sun, J. Li, H. Peng, J. Wu, Y. Ning, P. S. Yu, and L. He, “SUGAR: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism,” in WWW , 2021
2021
Closest in time.
2021
Closest in time.
S. Wan, S. Pan, J. Yang, and C. Gong, “Contrastive and generative graph convolutional networks for graph-based semi-supervised learning,” in AAAI , vol. 35, no. 11, 2021, pp. 10 049–10 057
2021
Closest in time.
2021
Closest in time.
M. Xu, H. Wang, B. Ni, H. Guo, and J. Tang, “Self-supervised graph-level representation learning with local and global structure,” in ICML . PMLR, 2021
2021
Closest in time.
B. Jing, C. Park, and H. Tong, “HDMI: High-order deep multiplex infomax,” in WWW , 2021, pp. 2414–2424
2021
Closest in time.
2021
Closest in time.
K. K. Roy, A. Roy, A. Rahman, M. A. Amin, and A. A. Ali, “Node embedding using mutual information and self-supervision based bi-level aggregation,” in IJCNN , 2021
2021
Closest in time.
S. Kou, W. Xia, X. Zhang, Q. Gao, and X. Gao, “Self-supervised graph convolutional clustering by preserving latent distribution,” Neurocomputing , vol. 437, pp. 218–226, 2021
2021
Closest in time.
B. Hao, J. Zhang, H. Yin, C. Li, and H. Chen, “Pre-training graph neural networks for cold-start users and items representation,” in WSDM , 2021, pp. 265–273
2021
Closest in time.
J. Yu, H. Yin, J. Li, Q. Wang, N. Q. V. Hung, and X. Zhang, “Self-supervised multi-channel hypergraph convolutional network for social recommendation,” in WWW , 2021, pp. 413–424
2021
Closest in time.
X. Xia, H. Yin, J. Yu, Q. Wang, L. Cui, and X. Zhang, “Self-supervised hypergraph convolutional networks for session-based recommendation,” in AAAI , vol. 35, no. 5, 2021
2021
Closest in time.
2021
Closest in time.
Y. Liu, S. Yang, C. Lei, G. Wang, H. Tang, J. Zhang, A. Sun, and C. Miao, “Pre-training graph transformer with multimodal side information for recommendation,” in ACM Multimedia , 2021
2021
Closest in time.
Y. Liu, Z. Li, S. Pan, C. Gong, C. Zhou, and G. Karypis, “Anomaly detection on attributed networks via contrastive self-supervised learning,” IEEE TNNLS , 2021
2021
Closest in time.
M. Jin, Y. Liu, Y. Zheng, L. Chi, Y.-F. Li, and S. Pan, “Anemone: Graph anomaly detection with multi-scale contrastive learning,” in CIKM , 2021, pp. 3122–3126
2021
Closest in time.
Y. Zheng, M. Jin, Y. Liu, L. Chi, K. T. Phan, and Y.-P. P. Chen, “Generative and contrastive self-supervised learning for graph anomaly detection,” IEEE TKDE , 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
S. Cheng, L. Zhang, B. Jin, Q. Zhang, X. Lu, M. You, and X. Tian, “GraphMS: Drug target prediction using graph representation learning with substructures,” Applied Sciences , 2021
2021
Closest in time.
Y. Wang, Y. Min, X. Chen, and J. Wu, “Multi-view graph contrastive representation learning for drug-drug interaction prediction,” in WWW , 2021, pp. 2921–2933
2021
Closest in time.
D. Jin, Z. Yu, P. Jiao, S. Pan, P. S. Yu, and W. Zhang, “A survey of community detection approaches: From statistical modeling to deep learning,” IEEE TKDE , 2021
2021
Closest in time.
B. Fatemi, L. E. Asri, and S. M. Kazemi, “SLAPS: Self-supervision improves structure learning for graph neural networks,” in NeurIPS , 2021
2021
Closest in time.
C. Liu, L. Wen, Z. Kang, G. Luo, and L. Tian, “Self-supervised consensus representation learning for attributed graph,” in ACM Multimedia , 2021, pp. 2654–2662
2021
Closest in time.
2021
Closest in time.
L. Sun, K. Yu, and K. Batmanghelich, “Context matters: Graph-based self-supervised representation learning for medical images,” in AAAI , vol. 35, no. 6, May 2021, pp. 4874–4882
2021
Closest in time.
Y. Tan, G. Long, L. Liu, T. Zhou, Q. Lu, J. Jiang, and C. Zhang, “Fedproto: Federated prototype learning over heterogeneous devices,” in AAAI , 2022
2022
Closest in time.