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Gu and Han |
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1 |
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2012a |
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リンク |
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| Unsupervised Feature Selection for Linked Social Media Data |
Tang and Liu |
2012b |
リンク |
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Li et al |
2016 |
リンク |
6 |
| Unsupervised Feature Selection for Multi-View Data in Social Media |
Tang et al |
2013b |
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| Unsupervised Feature Selection in Signed Social Networks |
Cheng et al |
2017 |
リンク |
8 |
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Huang et al |
2020 |
リンク |
9 |
| Adaptive Unsupervised Feature Selection on Attributed Networks |
Li et al |
2019b |
リンク |
10 |
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Shi and Malik |
2000 |
PDF |
11 |
| On Spectral Clustering: Analysis and an algorithm |
Ng et al |
2002 |
リンク |
12 |
| Laplacian Eigenmaps for Dimensionality Reduction and Data Representation |
Belkin and Niyogi |
2003 |
PDF |
13 |
| A Global Geometric Framework for Nonlinear Dimensionality Reduction |
Tenenbaum et al |
2000 |
PDF |
14 |
| Nonlinear Dimensionality Reduction by Locally Linear Embedding |
Roweis and Saul |
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PDF |
15 |
| Combining Content and Link for Classification using Matrix Factorization |
Zhu et al |
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PDF |
16 |
| Exploiting Homophily Effect for Trust Prediction |
Tang et al |
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リンク |
17 |
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PDF |
18 |
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PDF |
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Tang et al |
2016a |
リンク |
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| Link Prediction via Matrix Factorization |
Menon and Elkan |
2011 |
リンク |
21 |
| Community discovery using nonnegative matrix factorization |
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| Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec |
Qiu et al |
2018b |
arxiv |
23 |
| Distributed Representations of Words and Phrases and their Compositionality |
Mikolov et al |
2013 |
arxiv |
24 |
| DeepWalk: Online Learning of Social Representations |
Perozzi et al |
2014 |
arxiv |
25 |
| LINE: Large-scale Information Network Embedding |
Tang et al |
2015 |
arxiv |
26 |
| node2vec: Scalable Feature Learning for Networks |
Grover and Leskovec |
2016 |
arxiv |
27 |
| GraRep: Learning Graph Representations with Global Structural Information |
Cao et al |
2015 |
リンク |
28 |
| struc2vec: Learning Node Representations from Structural Identity |
Ribeiro et al |
2017 |
arxiv |
29 |
| Community Preserving Network Embedding |
Wang et al |
2017c |
リンク |
30 |
| Preserving Local and Global Information for Network Embedding |
Ma et al |
2017 |
arxiv |
31 |
| PRUNE: Preserving Proximity and Global Ranking for Network Embedding |
Lai et al |
2017 |
リンク |
32 |
| RaRE: Social Rank Regulated Large-scale Network Embedding |
Gu et al |
2018 |
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33 |
| Asymmetric Transitivity Preserving Graph Embedding |
Ou et al |
2016 |
PDF |
34 |
| Heterogeneous Network Embedding via Deep Architectures |
Chang et al |
2015 |
リンク |
35 |
| metapath2vec: Scalable Representation Learning for Heterogeneous Networks |
Dong et al |
2017 |
リンク |
36 |
| BiNE: Bipartite Network Embedding |
Gao et al |
2018b |
PDF |
37 |
| Multi-Dimensional Network Embedding with Hierarchical Structure |
Ma et al |
2018d |
リンク |
38 |
| Signed Network Embedding in Social Media |
Wang et al |
2017b |
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39 |
| Structural Deep Embedding for Hyper-Networks |
Tu et al |
2018 |
arxiv |
40 |
| Continuous-Time Dynamic Network Embeddings |
Nguyen et al |
2018 |
リンク |
41 |
| Attributed Network Embedding for Learning in a Dynamic Environment |
Li et al |
2017a |
arxiv |
42 |
| Graph neural networks for ranking Web pages |
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43 |
| Spectral Networks and Locally Connected Networks on Graphs |
Bruna et al |
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arxiv |
44 |
| Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering |
Defferrard et al |
2016 |
arxiv |
45 |
| Semi-Supervised Classification with Graph Convolutional Networks |
Kipf and Welling |
2016a |
arxiv |
46 |
| Diffusion-Convolutional Neural Networks |
Atwood and Towsley |
2016 |
arxiv |
47 |
| Learning Convolutional Neural Networks for Graphs |
Niepert et al |
2016 |
arxiv |
48 |
| Neural Message Passing for Quantum Chemistry |
Gilmer et al |
2017 |
arxiv |
49 |
| Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks |
Monti et al |
2017 |
arxiv |
50 |
| Graph Attention Networks |
Veličković et al |
2017 |
arxiv |
51 |
| Inductive Representation Learning on Large Graphs |
Hamilton et al |
2017a |
arxiv |
52 |
| Gated Graph Sequence Neural Networks |
Li et al |
2015 |
arxiv |
53 |
| Hierarchical Graph Representation Learning with Differentiable Pooling |
Ying et al |
2018c |
arxiv |
54 |
| Graph U-Nets |
Gao and Ji |
2019 |
arxiv |
55 |
| Graph Convolutional Networks with EigenPooling |
Ma et al |
2019b |
arxiv |
56 |
| Adversarial Attacks on Neural Networks for Graph Data |
Zügner et al |
2018 |
arxiv |
57 |
| Adversarial Attacks on Graph Neural Networks via Meta Learning |
Zügner and Günnemann |
2019 |
arxiv |
58 |
| Adversarial Attack on Graph Structured Data |
Dai et al |
2018 |
arxiv |
59 |
| Attacking Graph Convolutional Networks via Rewiring |
Ma et al |
2019d |
arxiv |
60 |
| Robust Graph Convolutional Networks Against Adversarial Attacks |
Zhu et al |
2019a |
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61 |
| Transferring Robustness for Graph Neural Network Against Poisoning Attacks |
Tang et al |
2019 |
arxiv |
62 |
| Graph Structure Learning for Robust Graph Neural Networks |
Jin et al |
2020b |
arxiv |
63 |
| Stochastic Training of Graph Convolutional Networks with Variance Reduction |
Chen et al |
2018a |
arxiv |
64 |
| FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling |
Chen et al |
2018b |
arxiv |
65 |
| Adaptive Sampling Towards Fast Graph Representation Learning |
Huang et al |
2018 |
arxiv |
66 |
| Deep Collective Classification in Heterogeneous Information Networks |
Zhang et al |
2018b |
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67 |
| Heterogeneous Graph Attention Network |
Wang et al |
2019i |
arxiv |
68 |
| ActiveHNE: Active Heterogeneous Network Embedding |
Chen et al |
2019b |
arxiv |
69 |
| Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs |
He et al |
2019 |
arxiv |
70 |
| Multi-dimensional Graph Convolutional Networks |
Ma et al |
2019c |
arxiv |
71 |
| Signed Graph Convolutional Network |
Derr et al |
2018 |
arxiv |
72 |
| Hypergraph Neural Networks |
Feng et al |
2019b |
arxiv |
73 |
| HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs |
Yadati et al |
2019 |
リンク |
74 |
| EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs |
Pareja et al |
2019 |
arxiv |
75 |
| Structural Deep Network Embedding |
Wang et al |
2016 |
リンク |
76 |
| Deep Neural Networks for Learning Graph Representations |
Cao et al |
2016 |
リンク |
77 |
| Variational Graph Auto-Encoders |
Kipf and Welling |
2016b |
arxiv |
78 |
| Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks |
Tai et al |
2015 |
arxiv |
79 |
| Semantic Object Parsing with Graph LSTM |
Liang et al |
2016 |
arxiv |
80 |
| GraphGAN: Graph Representation Learning with Generative Adversarial Nets |
Wang et al |
2018a |
arxiv |
81 |