# Graph and Social Network Representation

> Including **Graph Representation**, **Social Network Representation**, **Graph Convolutional Network** and etc.

## Source
- GitHub Link: [[thunlp/GNNPapers]](https://github.com/thunlp/GNNPapers).
- GitHub Link: [[DeepGraphLearning/LiteratureDL4Graph]](https://github.com/DeepGraphLearning/LiteratureDL4Graph).
- GitHub Link: [[rusty1s/pytorch_geometric]](https://github.com/rusty1s/pytorch_geometric).
- GitHub Link: [[Jiakui/awesome-gcn]](https://github.com/Jiakui/awesome-gcn)

## Tutorial and Explanation
- **How to understand Graph Convolutional Networks (GCN)**, [[zhihu blog]](https://www.zhihu.com/question/54504471).
- **Graph Convolutional Networks (GCN) guide for beginner**, [[zhihu blog]](https://zhuanlan.zhihu.com/p/54505069).
- **Explained Graph Convolutional Networks (GCN)**, [[zhihu blog]](https://zhuanlan.zhihu.com/p/67522582).
- **Spectral Clustering Summary**, [[blog]](https://www.cnblogs.com/pinard/p/6221564.html).
- **Convolutional Neural Networks on Graphs**, [[NTU slides]](http://www.ntu.edu.sg/home/xbresson/pdf/talk_xbresson_gcnn.pdf).

## Survey
- [2017 SPM] **Geometric deep learning: going beyond Euclidean data**, [[paper]](https://arxiv.org/pdf/1611.08097.pdf), [[bibtex]](/Bibtex/Geometric%20deep%20learning%20-%20going%20beyond%20Euclidean%20data.bib).
- [2018 ArXiv] **Deep Learning on Graphs: A Survey**, [[paper]](https://arxiv.org/pdf/1812.04202.pdf), [[bibtex]](/Bibtex/Deep%20Learning%20on%20Graphs%20-%20A%20Survey.bib), [[blog]](http://deeplearningresource.com/category/图神经网络/).
- [2018 ArXiv] **Relational inductive biases, deep learning, and graph networks**, [[paper]](https://arxiv.org/pdf/1806.01261.pdf), [[bibtex]](/Bibtex/Relational%20inductive%20biases%20deep%20learning%20and%20graph%20networks.bib), sources: [[deepmind/graph_nets]](https://github.com/deepmind/graph_nets).
- [2019 ArXiv] **Graph Neural Networks: A Review of Methods and Applications**, [[paper]](https://arxiv.org/pdf/1812.08434.pdf), [[bibtex]](/Bibtex/Graph%20Neural%20Networks%20-%20A%20Review%20of%20Methods%20and%20Applications.bib), [[blog1]](https://daiwk.github.io/posts/dl-gnn.html), [[zhihu]](https://zhuanlan.zhihu.com/p/53476124), [[blog2]](https://mp.weixin.qq.com/s?__biz=MzIwMTc4ODE0Mw==&mid=2247493906&idx=1&sn=15c9f18a1ce6baa15dc85ecb52e799f6&chksm=96ea3692a19dbf847c1711e6e194ad60d80d11138daf0938f90489a054d77cfd523bee2dc1d2&mpshare=1&scene=1&srcid=1226x1tdeDKTzwcuszAuh1O0&pass_ticket=SzwEuJ2YzNx6f6qRO8kkEzvCJzmwLfJnp2tSTApgym2Eec7lBnwb3SzGw6NeSiqz#rd), [[blog3]](https://www.jiqizhixin.com/articles/2018-12-27-11), sources: [[thunlp/GNNPapers]](https://github.com/thunlp/GNNPapers).
- [2019 ArXiv] **A Comprehensive Survey on Graph Neural Networks**, [[paper]](https://arxiv.org/pdf/1901.00596.pdf), [[bibtex]](/Bibtex/A%20Comprehensive%20Survey%20on%20Graph%20Neural%20Networks.bib).

## Graph Embedding
- [2014 KDD] **DeepWalk: Online Learning of Social Representations**, [[paper]](http://www.perozzi.net/publications/14_kdd_deepwalk.pdf), [[bibtex]](/Bibtex/DeepWalk%20-%20Online%20Learning%20of%20Social%20Representations.bib), [[blog]](https://zhuanlan.zhihu.com/p/56380812), sources: [[phanein/deepwalk]](https://github.com/phanein/deepwalk).
- [2015 WWW] **LINE: Large-scale Information Network Embedding**, [[paper]](http://www.www2015.it/documents/proceedings/proceedings/p1067.pdf), [[bibtex]](/Bibtex/LINE%20-%20Large-scale%20Information%20Network%20Embedding.bib), [[blog]](https://zhuanlan.zhihu.com/p/56478167), sources: [[tangjianpku/LINE]](https://github.com/tangjianpku/LINE), [[shenweichen/GraphEmbedding]](https://github.com/shenweichen/GraphEmbedding).
- [2016 KDD] **node2vec: Scalable Feature Learning for Networks**, [[paper]](https://www.kdd.org/kdd2016/papers/files/rfp0218-groverA.pdf), [[bibtex]](/Bibtex/node2vec%20-%20Scalable%20Feature%20Learning%20for%20Networks.bib), [[homepage]](https://snap.stanford.edu/node2vec/), [[blog]](https://zhuanlan.zhihu.com/p/56542707), [[blog]](https://zhuanlan.zhihu.com/p/64756917), [[blog]](https://zhuanlan.zhihu.com/p/63631102), sources: [[snap-stanford/snap/examples/node2vec]](https://github.com/snap-stanford/snap/tree/master/examples/node2vec), [[aditya-grover/node2vec]](https://github.com/aditya-grover/node2vec), [[shenweichen/GraphEmbedding]](https://github.com/shenweichen/GraphEmbedding).
- [2016 KDD] **Structural Deep Network Embedding**, [[paper]](https://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdf), [[bibtex]](/Bibtex/Structural%20Deep%20Network%20Embedding.bib), [[blog]](https://zhuanlan.zhihu.com/p/56637181), [[blog]](https://zhuanlan.zhihu.com/p/33732033), sources: [[suanrong/SDNE]](https://github.com/suanrong/SDNE), [[ZhangWeiguo/sdne]](https://github.com/ZhangWeiguo/sdne), [[shenweichen/GraphEmbedding]](https://github.com/shenweichen/GraphEmbedding).
- [2017 KDD] **struc2vec: Learning Node Representations from Structural Identity**, [[paper]](https://arxiv.org/pdf/1704.03165.pdf), [[bibtex]](/Bibtex/struc2vec%20-%20Learning%20Node%20Representations%20from%20Structural.bib), [[blog]](https://zhuanlan.zhihu.com/p/56733145), [[blog]](https://zhuanlan.zhihu.com/p/63175042), sources: [[leoribeiro/struc2vec]](https://github.com/leoribeiro/struc2vec), [[shenweichen/GraphEmbedding]](https://github.com/shenweichen/GraphEmbedding).
- [2018 ICML] **Anonymous Walk Embeddings**, [[paper]](http://proceedings.mlr.press/v80/ivanov18a/ivanov18a.pdf), [[bibtex]](/Bibtex/Anonymous%20Walk%20Embeddings.bib), sources: [[nd7141/AWE]](https://github.com/nd7141/AWE).
- [2018 IJCAI] **Adversarially Regularized Graph Autoencoder for Graph Embedding**, [[paper]](https://www.ijcai.org/proceedings/2018/0362.pdf), [[bibtex]](/Bibtex/Adversarially%20Regularized%20Graph%20Autoencoder%20for%20Graph%20Embedding.bib), sources: [[Ruiqi-Hu/ARGA
]](https://github.com/Ruiqi-Hu/ARGA).
- [2018 NeurIPS] **Hierarchical Graph Representation Learning with Differentiable Pooling**, [[paper]](https://papers.nips.cc/paper/2018/file/e77dbaf6759253c7c6d0efc5690369c7-Paper.pdf), [[bibtex]](/Bibtex/Hierarchical%20Graph%20Representation%20Learning%20with%20Differentiable%20Pooling.bib), sources: [[RexYing/diffpool]](https://github.com/RexYing/diffpool).
- [2018 ICLR] **Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking**, [[paper]](https://openreview.net/pdf?id=r1ZdKJ-0W), [[bibtex]](/Bibtex/Deep%20Gaussian%20Embedding%20of%20Graphs%20-%20Unsupervised%20Inductive%20Learning%20via%20Ranking.bib), [[poster]](https://www.in.tum.de/fileadmin/w00bws/daml/g2g/g2g_poster_iclr18.pdf), sources: [[abojchevski/graph2gauss]](https://github.com/abojchevski/graph2gauss).
- [2018 KDD] **Deep Recursive Network Embedding with Regular Equivalence**, [[paper]](http://pengcui.thumedialab.com/papers/NE-RegularEquivalence.pdf), [[bibtex]](/Bibtex/Deep%20Recursive%20Network%20Embedding%20with%20Regular%20Equivalence.bib), sources: [[tadpole/DRNE]](https://github.com/tadpole/DRNE).
- [2018 KDD] **Deep Variational Network Embedding in Wasserstein Space**, [[paper]](http://pengcui.thumedialab.com/papers/NE-DeepVariational.pdf), [[bibtex]](/Bibtex/Deep%20Variational%20Network%20Embedding%20in%20Wasserstein%20Space.bib).
- [2019 IJCAI] **ActiveHNE: Active Heterogeneous Network Embedding**, [[paper]](https://www.ijcai.org/proceedings/2019/0294.pdf), [[bibtex]](/Bibtex/ActiveHNE%20-%20Active%20Heterogeneous%20Network%20Embedding.bib).

## Graph Neural Network Models
- [2009 TNN] **The Graph Neural Network Model**, [[paper]](https://persagen.com/files/misc/scarselli2009graph.pdf), [[bibtex]](/Bibtex/The%20Graph%20Neural%20Network%20Model.bib), [[blog1]](https://blog.csdn.net/u011748542/article/details/86289511), [[blog2]](https://www.cnblogs.com/SivilTaram/archive/2019/06/02/graph_neural_network.html).
- [2009 TNN] **Neural Network for Graphs: A Contextual Constructive Approach**, [[paper]](/Documents/Papers/Neural%20Network%20for%20Graphs%20-%20A%20Contextual%20Constructive%20Approach.pdf), [[bibtex]](/Bibtex/Neural%20Network%20for%20Graphs%20-%20A%20Contextual%20Constructive%20Approach.bib).
- [2013 SPM] ***Extend Fourier Transform to Graph***, **The Emerging Field of Signal Processing on Graphs**, [[paper]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.367.6064&rep=rep1&type=pdf), [[bibtex]](/Bibtex/The%20Emerging%20Field%20of%20Signal%20Processing%20on%20Graphs.bib), sources: [[mdbartos/graph-signals]](https://github.com/mdbartos/graph-signals).
- [2014 ICLR] ***1st Generation of Spectral GCN***, **Spectral Networks and Deep Locally Connected Networks on Graphs**, [[paper]](https://arxiv.org/pdf/1312.6203.pdf), [[bibtex]](/Bibtex/Spectral%20Networks%20and%20Deep%20Locally%20Connected%20Networks%20on%20Graphs.bib), [[blog]](https://blog.csdn.net/BVL10101111/article/details/53426226), sources: [[mdeff/cnn_graph]](https://github.com/mdeff/cnn_graph).
- [2015 ArXiv] **Deep Convolutional Networks on Graph-Structured Data**, [[paper]](https://arxiv.org/pdf/1506.05163.pdf), [[bibtex]](/Bibtex/Deep%20Convolutional%20Networks%20on%20Graph-Structured%20Data.bib).
- [2016 NIPS] ***2nd Generation of Spectral GCN***, **Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering**, [[paper]](https://papers.nips.cc/paper/6081-convolutional-neural-networks-on-graphs-with-fast-localized-spectral-filtering.pdf), [[bibtex]](/Bibtex/Convolutional%20Neural%20Networks%20on%20Graphs%20with%20Fast%20Localized%20Spectral%20Filtering.bib), sources: [[mdeff/cnn_graph]](https://github.com/mdeff/cnn_graph), [[xbresson/spectral_graph_convnets]](https://github.com/xbresson/spectral_graph_convnets).
- [2016 NIPS] **Diffusion-Convolutional Neural Networks**, [[paper]](https://arxiv.org/pdf/1511.02136.pdf), [[bibtex]](/Bibtex/Diffusion-Convolutional%20Neural%20Networks.bib), [[blog]](https://www.cnblogs.com/wangxiaocvpr/p/8763510.html), sources: [[jcatw/dcnn]](https://github.com/jcatw/dcnn), [[RicardoZiTseng/dcnn-tensorflow]](https://github.com/RicardoZiTseng/dcnn-tensorflow).
- [2016 ICML] **Learning Convolutional Neural Networks for Graphs**, [[paper]](http://proceedings.mlr.press/v48/niepert16.pdf), [[bibtex]](/Bibtex/Learning%20Convolutional%20Neural%20Networks%20for%20Graphs.bib)， [[blog1]](https://blog.csdn.net/zsfcg/article/details/82465973), [[blog2]](https://zhuanlan.zhihu.com/p/37840709), sources: [[seiya-kumada/patchy-san]](https://github.com/seiya-kumada/patchy-san), [[graph-tool]](https://graph-tool.skewed.de).
- [2016 ICLR] **Gated Graph Sequence Neural Networks**, [[paper]](https://arxiv.org/pdf/1511.05493.pdf), [[bibtex]](/Bibtex/Gated%20Graph%20Sequence%20Neural%20Networks.bib), [[slides1]](https://pdfs.semanticscholar.org/f82d/c99ae5d32af70bcf6a40402af1cd7181f162.pdf), [[slides2]](https://www.cs.toronto.edu/~yujiali/files/talks/iclr16_ggnn_talk.pdf), [[slides3]](http://snap.stanford.edu/proj/embeddings-www/files/nrltutorial-part2-gnns.pdf), [[blog1]](https://zhuanlan.zhihu.com/p/28170197), [[blog2]](https://blog.csdn.net/duyue3052/article/details/82625431#1%3AGG-NNs计算过程), [[blog3]](https://www.cnblogs.com/SivilTaram/archive/2019/06/02/graph_neural_network.html), sources: [[microsoft/gated-graph-neural-network-samples]](https://github.com/microsoft/gated-graph-neural-network-samples), [[JamesChuanggg/ggnn.pytorch]](https://github.com/JamesChuanggg/ggnn.pytorch).
- [2017 ICLR] ***3rd Generation of Spectral GCN***, **Semi-Supervised Classification with Graph Convolutional Networks**, [[paper]](https://openreview.net/pdf?id=SJU4ayYgl), [[bibtex]](/Bibtex/Semi-Supervised%20Classification%20with%20Graph%20Convolutional%20Networks.bib), sources: [[tkipf/gcn]](https://github.com/tkipf/gcn), [[tkipf/pygcn]](https://github.com/tkipf/pygcn).
- [2017 NIPS] **Inductive Representation Learning on Large Graphs**, [[paper]](https://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf), [[bibtex]](/Bibtex/Inductive%20Representation%20Learning%20on%20Large%20Graphs.bib), [[homepage]](http://snap.stanford.edu/graphsage/), sources: [[williamleif/GraphSAGE]](https://github.com/williamleif/GraphSAGE).
- [2017 CVPR] **Geometric deep learning on graphs and manifolds using mixture model CNNs**, [[paper]](https://www.zpascal.net/cvpr2017/Monti_Geometric_Deep_Learning_CVPR_2017_paper.pdf), [[bibtex]](/Bibtex/Geometric%20deep%20learning%20on%20graphs%20and%20manifolds%20using%20mixture%20model%20CNNs.bib), sources: [[pierrebaque/GeometricConvolutionsBench]](https://github.com/pierrebaque/GeometricConvolutionsBench), [[rusty1s/pytorch_geometric]](https://github.com/rusty1s/pytorch_geometric).
- [2018 NIPS] **Structure-Aware Convolutional Neural Networks**, [[paper]](https://papers.nips.cc/paper/7287-structure-aware-convolutional-neural-networks.pdf), [[bibtex]](/Bibtex/Structure-Aware%20Convolutional%20Neural%20Networks.bib), sources: [[vector-1127/SACNNs]](https://github.com/vector-1127/SACNNs).
- [2018 NIPS] **Bayesian Semi-supervised Learning with Graph Gaussian Processes**, [[paper]](https://papers.nips.cc/paper/7440-bayesian-semi-supervised-learning-with-graph-gaussian-processes.pdf), [[bibtex]](/Bibtex/Bayesian%20Semi-supervised%20Learning%20with%20Graph%20Gaussian%20Processes.bib), sources: [[yincheng/GGP]](https://github.com/yincheng/GGP).
- [2018 ICML] **GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models**, [[paper]](), [[bibtex]](/Bibtex/GraphRNN%20-%20Generating%20Realistic%20Graphs%20with%20Deep%20Auto-regressive%20Models.bib), [[slides]](http://www.cs.virginia.edu/~as5cu/files/graphRNN.pdf), sources: [[JiaxuanYou/graph-generation]](https://github.com/JiaxuanYou/graph-generation).
- [2018 ICML] **NetGAN: Generating Graphs via Random Walks**, [[paper]](https://arxiv.org/pdf/1803.00816.pdf), [[bibtex]](/Bibtex/NetGAN%20-%20Generating%20Graphs%20via%20Random%20Walks.bib), [[slides]](https://qdata.github.io/deep2Read//talks2019/Extra19s/TkachNetGAN.pdf), [[poster]](https://www.in.tum.de/fileadmin/w00bws/daml/netgan/netgan_icml_poster.pdf), sources: [[danielzuegner/netgan]](https://github.com/danielzuegner/netgan).
- [2018 ICLR] **Graph Attention Networks**, [[paper]](https://openreview.net/pdf?id=rJXMpikCZ), [[bibtex]](/Bibtex/Graph%20Attention%20Networks.bib), sources: [[PetarV-/GAT]](https://github.com/PetarV-/GAT), [[Diego999/pyGAT]](https://github.com/Diego999/pyGAT), [[danielegrattarola/keras-gat]](https://github.com/danielegrattarola/keras-gat).
- [2018 ICLR] **FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling**, [[paper]](https://openreview.net/pdf?id=rytstxWAW), [[bibtex]](/Bibtex/FastGCN%20-%20Fast%20Learning%20with%20Graph%20Convolutional%20Networks%20via%20Importance%20Sampling.bib), sources: [[matenure/FastGCN]](https://github.com/matenure/FastGCN).
- [2019 ICLR] **Capsule Graph Neural Network**, [[paper]](https://openreview.net/pdf?id=Byl8BnRcYm), [[bibtex]](/Bibtex/Capsule%20Graph%20Neural%20Network.bib), sources: [[XinyiZ001/CapsGNN]](https://github.com/XinyiZ001/CapsGNN), [[benedekrozemberczki/CapsGNN]](https://github.com/benedekrozemberczki/CapsGNN).
- [2019 NeurIPS] **Graph Transformer Networks**, [[paper]](https://papers.nips.cc/paper/9367-graph-transformer-networks.pdf), [[bibtex]](/Bibtex/Graph%20Transformer%20Networks.bib).

## Applications
- [2017 ACL] **CANE: Context-Aware Network Embedding for Relation Modeling**, [[paper]](https://www.aclweb.org/anthology/P17-1158.pdf), [[bibtex]](/Bibtex/CANE%20-%20Context-Aware%20Network%20Embedding%20for%20Relation%20Modeling.bib), [[slides]](http://nlp.csai.tsinghua.edu.cn/~tcc/publications/cane_acl.pdf), sources: [[thunlp/CANE
]](https://github.com/thunlp/CANE).
- [2017 ICML] **Neural Message Passing for Quantum Chemistry**, [[paper]](https://arxiv.org/pdf/1704.01212.pdf), [[bibtex]](/Bibtex/Neural%20Message%20Passing%20for%20Quantum%20Chemistry.bib), sources: [[microsoft/gated-graph-neural-network-samples]](https://github.com/Microsoft/gated-graph-neural-network-samples), [[priba/nmp_qc]](https://github.com/priba/nmp_qc).
- [2017 NIPS] **Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks**, [[paper]](https://papers.nips.cc/paper/6960-geometric-matrix-completion-with-recurrent-multi-graph-neural-networks.pdf), [[bibtex]](/Bibtex/Geometric%20Matrix%20Completion%20with%20Recurrent%20Multi-Graph%20Neural%20Networks.bib), sources: [[fmonti/mgcnn]](https://github.com/fmonti/mgcnn).
- [2018 EMNLP] **Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks**, [[paper]](https://www.aclweb.org/anthology/D18-1032.pdf), [[bibtex]](/Bibtex/Cross-lingual%20Knowledge%20Graph%20Alignment%20via%20Graph%20Convolutional%20Networks.bib), sources: [[muhaochen/MTransE]](https://github.com/muhaochen/MTransE), [[1049451037/GCN-Align]](https://github.com/1049451037/GCN-Align).
- [2018 KDD] **Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks**, [[paper]](https://yu-shi-homepage.github.io/kdd18.pdf), [[bibtex]](/Bibtex/Easing%20Embedding%20Learning%20by%20Comprehensive%20Transcription%20of%20Heterogeneous%20Information%20Networks.bib), sources: [[GentleZhu/HEER]](https://github.com/GentleZhu/HEER).
- [2018 WWW] **Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification**, [[paper]](https://persagen.com/files/misc/zhuang2018dual.pdf), [[bibtex]](/Bibtex/Dual%20Graph%20Convolutional%20Networks%20for%20Graph-Based%20Semi-Supervised%20Classification.bib), sources: [[ZhuangCY/DGCN]](https://github.com/ZhuangCY/DGCN).
- [2018 ACL] **Graph-to-Sequence Learning using Gated Graph Neural Networks**, [[paper]](https://www.aclweb.org/anthology/P18-1026.pdf), [[bibtex]](/Bibtex/Graph-to-Sequence%20Learning%20using%20Gated%20Graph%20Neural%20Networks.bib), [[poster]](https://www.aclweb.org/anthology/attachments/P18-1026.Poster.pdf), sources: [[beckdaniel/acl2018_graph2seq]](https://github.com/beckdaniel/acl2018_graph2seq).
- [2019 ACL] **Improving Textual Network Embedding with Global Attention via Optimal Transport**, [[paper]](https://www.aclweb.org/anthology/P19-1512.pdf), [[bibtex]](/Bibtex/Improving%20Textual%20Network%20Embedding%20with%20Global%20Attention%20via%20Optimal%20Transport.bib), [[supplementary]](https://www.aclweb.org/anthology/attachments/P19-1512.Supplementary.pdf).
- [2019 ACL] **Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks**, [[paper]](https://www.aclweb.org/anthology/P19-1320.pdf), [[bibtex]](/Bibtex/Incorporating%20Syntactic%20and%20Semantic%20Information%20in%20Word%20Embeddings%20using%20Graph%20Convolutional%20Networks.bib), [[supplementary]](https://www.aclweb.org/anthology/attachments/P19-1320.Supplementary.pdf), sources: [[malllabiisc/WordGCN]](https://github.com/malllabiisc/WordGCN).
- [2019 ACL] **Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning**, [[paper]](https://www.aclweb.org/anthology/Q19-1019.pdf), [[bibtex]](https://www.aclweb.org/anthology/Q19-1019.bib), sources: [[Cartus/DCGCN]](https://github.com/Cartus/DCGCN).
- [2019 CVPR] **Rethinking Knowledge Graph Propagation for Zero-Shot Learning**, [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Kampffmeyer_Rethinking_Knowledge_Graph_Propagation_for_Zero-Shot_Learning_CVPR_2019_paper.pdf), [[bibtex]](/Bibtex/Rethinking%20Knowledge%20Graph%20Propagation%20for%20Zero-Shot%20Learning.bib), sources: [[cyvius96/DGP]](https://github.com/cyvius96/DGP).
- [2019 IJCAL] **Graph WaveNet for Deep Spatial-Temporal Graph Modeling**, [[paper]](https://www.ijcai.org/proceedings/2019/0264.pdf), [[bibtex]](/Bibtex/Graph%20WaveNet%20for%20Deep%20Spatial-Temporal%20Graph%20Modeling.bib), sources: [[nnzhan/Graph-WaveNet]](https://github.com/nnzhan/Graph-WaveNet).

## Others
- [2017 NIPS] **Learning Combinatorial Optimization Algorithms over Graphs**, [[paper]](https://papers.nips.cc/paper/7214-learning-combinatorial-optimization-algorithms-over-graphs.pdf), [[bibtex]](/Bibtex/Learning%20Combinatorial%20Optimization%20Algorithms%20over%20Graphs.bib), sources: [[Hanjun-Dai/graph_comb_opt]](https://github.com/Hanjun-Dai/graph_comb_opt).
- [2018 ICLR] **Mean-field theory of graph neural networks in graph partitioning**, [[paper]](https://papers.nips.cc/paper/7689-mean-field-theory-of-graph-neural-networks-in-graph-partitioning.pdf), [[bibtex]](/Bibtex/Mean-field%20theory%20of%20graph%20neural%20networks%20in%20graph%20partitioning.bib).
- [2018 ICML] **Representation Learning on Graphs with Jumping Knowledge Networks**, [[paper]](http://proceedings.mlr.press/v80/xu18c/xu18c.pdf), [[bibtex]](/Bibtex/Representation%20Learning%20on%20Graphs%20with%20Jumping%20Knowledge%20Networks.bib), sources: [[ShinKyuY/Representation_Learning_on_Graphs_with_Jumping_Knowledge_Networks]](https://github.com/ShinKyuY/Representation_Learning_on_Graphs_with_Jumping_Knowledge_Networks).

## Knowledge Base/Graph Completion and Representation
- Resources: [[shengyp/Temporal-and-Evolving-KG]](https://github.com/shengyp/Temporal-and-Evolving-KG).
- [2013 NIPS] **Reasoning With Neural Tensor Networks for Knowledge Base Completion**, [[paper]](https://nlp.stanford.edu/pubs/SocherChenManningNg_NIPS2013.pdf), [[bibtex]](/Bibtex/Reasoning%20With%20Neural%20Tensor%20Networks%20for%20Knowledge%20Base%20Completion.bib), sources: [[siddharth-agrawal/Neural-Tensor-Network]](https://github.com/siddharth-agrawal/Neural-Tensor-Network), [[dddoss/tensorflow-socher-ntn]](https://github.com/dddoss/tensorflow-socher-ntn).
- [2013 NIPS] **TransE: Translating Embeddings for Modeling Multi-relational Data**, [[paper]](https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf), [[bibtex]](/Bibtex/TransE.bib), sources: [[thunlp/TensorFlow-TransX]](https://github.com/thunlp/TensorFlow-TransX).
- [2014 AAAI] **TransH: Knowledge Graph Embedding by Translating on Hyperplanes**, [[paper]](https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8531/8546), [[bibtex]](/Bibtex/TransH.bib), sources: [[thunlp/TensorFlow-TransX]](https://github.com/thunlp/TensorFlow-TransX).
- [2015 EMNLP] **PTransE: Modeling Relation Paths for Representation Learning of Knowledge Bases**, [[paper]](https://www.aclweb.org/anthology/D15-1082.pdf), [[bibtex]](https://www.aclweb.org/anthology/D15-1082.bib), [[homepage]](https://github.com/thunlp), sources: [[thunlp/Fast-TransX]](https://github.com/thunlp/Fast-TransX).
- [2015 AAAI] **TransR: Learning Entity and Relation Embeddings for Knowledge Graph Completion**, [[paper]](https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9571/9523), [[bibtex]](/Bibtex/TransR.bib), sources: [[thunlp/TensorFlow-TransX]](https://github.com/thunlp/TensorFlow-TransX).
- [2015 ACL] **TransD: Knowledge Graph Embedding via Dynamic Mapping Matrix**, [[paper]](https://www.aclweb.org/anthology/P15-1067.pdf), [[bibtex]](https://www.aclweb.org/anthology/P15-1067.bib), sources: [[thunlp/TensorFlow-TransX]](https://github.com/thunlp/TensorFlow-TransX).
- [2016 AAAI] **Knowledge Graph Completion with Adaptive Sparse Transfer Matrix**, [[paper]](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11982/11693), [[bibtex]](/Bibtex/Knowledge%20Graph%20Completion%20with%20Adaptive%20Sparse%20Transfer%20Matrix.bib), sources: [[FrankWork/transparse]](https://github.com/FrankWork/transparse), [[thunlp/Fast-TransX]](https://github.com/thunlp/Fast-TransX).
- [2016 ACL] **Commonsense Knowledge Base Completion**, [[paper]](https://www.aclweb.org/anthology/P16-1137.pdf), [[bibtex]](https://www.aclweb.org/anthology/P16-1137.bib), [[homepage]](http://ttic.uchicago.edu/~kgimpel/commonsense.html), sources: [[Lorraine333/ACL_CKBC]](https://github.com/Lorraine333/ACL_CKBC).
- [2017 CIKM] **Commonsense for Machine Intelligence: Text to Knowledge and Knowledge to Text**, [[slides]](http://people.mpi-inf.mpg.de/~ntandon/presentations/cikm-2017-tutorial-commonsense/commonsense.pdf), [[CIKM 2017 Singapore Tutorials]](http://cikm2017.org/tutorialmain.html), [[Commonsense for Machine Intelligence, Allen Institute, CIKM 2017 TUTORIAL]](http://allenai.org/tutorials/csk/), [[Allen Institute]](http://allenai.org/index.html).
- [2017 AKBC] **RelNet: End-to-End Modeling of Entities & Relations**, [[paper]](https://arxiv.org/pdf/1706.07179.pdf), [[bibtex]](/Bibtex/RelNet.bib), [[homepage]](http://thetb.github.io).
- [2017 EMNLP] **Context-Aware Representations for Knowledge Base Relation Extraction**, [[paper]](https://www.aclweb.org/anthology/D17-1188.pdf), [[bibtex]](https://www.aclweb.org/anthology/D17-1188.bib), sources: [[UKPLab/emnlp2017-relation-extraction]](https://github.com/UKPLab/emnlp2017-relation-extraction).
- [2017 IJCAI] **Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach**, [[paper]](https://www.ijcai.org/proceedings/2017/0250.pdf), [[bibtex]](/Bibtex/Knowledge%20Transfer%20for%20Out-of-Knowledge-Base%20Entities%20-%20A%20Graph%20Neural%20Network%20Approach.bib), [[blog]](https://zhuanlan.zhihu.com/p/39822646).
- [2017 AAAI] **ConceptNet 5.5: An Open Multilingual Graph of General Knowledge**, [[paper]](https://arxiv.org/pdf/1612.03975.pdf), [[bibtex]](/Bibtex/ConceptNet%20-%20An%20Open%20Multilingual%20Graph%20of%20General%20Knowledge.bib), sources: [[GitHub page]](https://github.com/commonsense), [[commonsense/conceptnet5]](https://github.com/commonsense/conceptnet5), [[commonsense/conceptnet-numberbatch]](https://github.com/commonsense/conceptnet-numberbatch).
- [2018 AAAI] **SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings**, [[paper]](https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16839/15882), [[bibtex]](/Bibtex/Discovering%20Conceptual%20Primitives%20for%20Sentiment%20Analysis%20by%20Means%20of%20Context%20Embeddings.bib), sources: [[IsaacChanghau/ConceptualPrimitives]](https://github.com/IsaacChanghau/ConceptualPrimitives).
- [2018 AAAI] **Convolutional 2D Knowledge Graph Embeddings**, [[paper]](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17366/15884), [[bibtex]](/Bibtex/Convolutional%202D%20Knowledge%20Graph%20Embeddings.bib), sources: [[TimDettmers/ConvE]](https://github.com/TimDettmers/ConvE).
- [2019 AAAI] **Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification**, [[paper]](https://gaotianyu1350.github.io/assets/aaai2019_hatt_paper.pdf), [[bibtex]](/Bibtex/Hybrid%20Attention-Based%20Prototypical%20Networks%20for%20Noisy%20Few-Shot%20Relation%20Classification.bib), sources: [[thunlp/HATT-Proto]](https://github.com/thunlp/HATT-Proto).
- [2019 AAAI] **ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning**, [[paper]](https://arxiv.org/pdf/1811.00146.pdf), [[bibtex]](/Bibtex/ATOMIC%20-%20An%20Atlas%20of%20Machine%20Commonsense%20for%20If-Then%20Reasoning.bib), souces: [[AMGrobelnik/MultiCOMET]](https://github.com/AMGrobelnik/MultiCOMET).
- [2020 ACL] **XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning**, [[paper]](https://www.aclweb.org/anthology/2020.emnlp-main.185.pdf), [[bibtex]](https://www.aclweb.org/anthology/2020.emnlp-main.185.bib), sources: [[cambridgeltl/xcopa]](https://github.com/cambridgeltl/xcopa).