/Awesome-Graph-Papers

Collect papers about graph

Awesome Graph Papers

I will collect articles about graphs (such as graph neural networks). Welcome to Star.

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In China, this URL will be faster: https://gitee.com/chengsen/Awesome-Graph-Papers

2020 Top Conference

Survey

  1. Introduction to graph neural networks (2020) Liu Z, Zhou J. \\ https://evo.services/introduction-to-graph-neural-networks/
  2. Graph Learning Approaches to Recommender Systems: A Review (2020) Wang S, Hu L, et al. \\ https://arxiv.org/abs/2004.11718
  3. Deep Learning on Graphs: A Survey (2020) Zhang Z, Cui P, et al. \\ http://arxiv.org/abs/1812.04202
  4. Adversarial Attack and Defense on Graph Data: A Survey (2020) Sun L, Dou Y, et al. \\ http://arxiv.org/abs/1812.10528
  5. A Survey on Knowledge Graph-Based Recommender Systems (2020) Guo Q, Zhuang F, et al. \\ http://arxiv.org/abs/2003.00911
  6. A Comprehensive Survey on Graph Neural Networks (2020) Wu Z, Pan S, et al. \\ doi: 10.1109/TNNLS.2020.2978386 http://arxiv.org/abs/1901.00596
  7. Graph Neural Networks: A Review of Methods and Applications (2019) Zhou J, Cui G, et al. \\ http://arxiv.org/abs/1812.08434
  8. Graph Kernels: A Survey (2019) Nikolentzos G, Siglidis G, et al. \\ http://arxiv.org/abs/1904.12218
  9. A Survey on Graph Processing Accelerators: Challenges and Opportunities (2019) Gui C, Zheng L, et al. \\ http://arxiv.org/abs/1902.10130
  10. Representation Learning on Graphs: Methods and Applications (2018) Hamilton WL, Ying R, et al. \\ http://arxiv.org/abs/1709.05584
  11. Relational inductive biases, deep learning, and graph networks (2018) Battaglia PW, Hamrick JB, et al. \\ http://arxiv.org/abs/1806.01261
  12. Network Representation Learning: A Survey (2018) Zhang D, Yin J, et al. \\ http://arxiv.org/abs/1801.05852
  13. Graph Embedding Techniques, Applications, and Performance: A Survey (2018) Goyal P, Ferrara E. \\ doi: 10.1016/j.knosys.2018.03.022 http://arxiv.org/abs/1705.02801
  14. Attention Models in Graphs: A Survey (2018) Lee JB, Rossi RA, et al. \\ http://arxiv.org/abs/1807.07984
  15. A Tutorial on Network Embeddings (2018) Chen H, Perozzi B, et al. \\ http://arxiv.org/abs/1808.02590
  16. A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications (2018) Cai H, Zheng VW, et al. \\ http://arxiv.org/abs/1709.07604
  17. Network representation learning: an overview (2017) Yang C, Liu Z, et al. \\ doi: 10.1360/N112017-00145 http://engine.scichina.com/doi/10.1360/N112017-00145
  18. Knowledge Graph Embedding: A Survey of Approaches and Applications (2017) Wang Q, Mao Z, et al. \\ doi: 10.1109/TKDE.2017.2754499 http://arxiv.org/abs/1611.08097
  19. Geometric deep learning: going beyond Euclidean data (2017) Bronstein MM, Bruna J, et al. \\ doi: 10.1109/MSP.2017.2693418
  20. A Survey on Network Embedding (2017) Cui P, Wang X, et al. \\ http://arxiv.org/abs/1711.08752
  21. Knowledge graph refinement: A survey of approaches and evaluation methods (2016) Paulheim H. \\ doi: 10.3233/SW-160218 https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/SW-160218

Graph Generation

  1. Bridging Knowledge Graphs to Generate Scene Graphs (2020) Zareian A, Karaman S, et al. \\ http://arxiv.org/abs/2001.02314
  2. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting (2020) Bai L, Yao L, et al. \\ http://arxiv.org/abs/2007.02842
  3. Query Graph Generation for Answering Multi-hop Complex Questions from Knowledge Bases (2020) Lan Y, Jiang J. \\ https://www.aclweb.org/anthology/2020.acl-main.91
  4. GPT-GNN: Generative Pre-Training of Graph Neural Networks (2020) Hu Z, Dong Y, et al. \\ http://arxiv.org/abs/2006.15437
  5. MoFlow: An Invertible Flow Model for Generating Molecular Graphs (2020) Zang C, Wang F. \\ http://arxiv.org/abs/2006.10137 \\ doi: 10.1145/3394486.3403104
  6. Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation (2020) He T, Gao L, et al. \\ http://arxiv.org/abs/2006.07585
  7. Structural Patterns and Generative Models of Real-world Hypergraphs (2020) Do MT, Yoon S, et al. \\ http://arxiv.org/abs/2006.07060 \\ doi: 10.1145/3394486.3403060
  8. CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training (2020) Guo Q, Jin Z, et al. \\ http://arxiv.org/abs/2006.04702
  9. Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation (2020) Knyazev B, de Vries H, et al. \\ http://arxiv.org/abs/2005.08230
  10. GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation (2020) Goyal N, Jain HV, et al. \\ doi: 10.1145/3366423.3380201
  11. Hierarchical Generation of Molecular Graphs using Structural Motifs (2020) Jin W, Barzilay R, et al. \\ http://arxiv.org/abs/2002.03230
  12. Deep Generative Probabilistic Graph Neural Networks for Scene Graph Generation (2020) Khademi M, Schulte O. \\ doi: 10.1609/aaai.v34i07.6783
  13. Weakly Supervised Visual Semantic Parsing (2020) Zareian A, Karaman S, et al. \\ http://arxiv.org/abs/2001.02359
  14. GPS-Net: Graph Property Sensing Network for Scene Graph Generation (2020) Lin X, Ding C, et al. \\ http://arxiv.org/abs/2003.12962
  15. Unbiased Scene Graph Generation from Biased Training (2020) Tang K, Niu Y, et al. \\ http://arxiv.org/abs/2002.11949
  16. Permutation Invariant Graph Generation via Score-Based Generative Modeling (2020) Niu C, Song Y, et al. \\ http://arxiv.org/abs/2003.00638
  17. MALOnt: An Ontology for Malware Threat Intelligence (2020) Rastogi N, Dutta S, et al. \\ http://arxiv.org/abs/2006.11446 \\ doi: 10.13140/RG.2.2.16426.64962
  18. Disentangling Interpretable Generative Parameters of Random and Real-World Graphs (2019) Stoehr N, Yilmaz E, et al. \\ http://arxiv.org/abs/1910.05639
  19. Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology (2019) Dehmamy N, Barabási A-L, et al. \\ http://arxiv.org/abs/1907.05008
  20. Image-Conditioned Graph Generation for Road Network Extraction (2019) Belli D, Kipf T. \\ http://arxiv.org/abs/1910.14388
  21. D-VAE: A Variational Autoencoder for Directed Acyclic Graphs (2019) Zhang M, Jiang S, et al. \\ http://arxiv.org/abs/1904.11088
  22. The Limited Multi-Label Projection Layer (2019) Amos B, Koltun V, et al. \\ http://arxiv.org/abs/1906.08707
  23. Graph Residual Flow for Molecular Graph Generation (2019) Honda S, Akita H, et al. \\ http://arxiv.org/abs/1909.13521
  24. NeVAE: A Deep Generative Model for Molecular Graphs (2019) Samanta B, De A, et al. \\ http://arxiv.org/abs/1802.05283
  25. TGG: Transferable Graph Generation for Zero-shot and Few-shot Learning (2019) Zhang C, Lyu X, et al. \\ http://arxiv.org/abs/1908.11503
  26. Encoding Robust Representation for Graph Generation (2019) Zou D, Lerman G. \\ doi: 10.1109/IJCNN.2019.8851705
  27. Labeled Graph Generative Adversarial Networks (2019) Fan S, Huang B. \\ http://arxiv.org/abs/1906.03220
  28. GraphNVP: An Invertible Flow Model for Generating Molecular Graphs (2019) Madhawa K, Ishiguro K, et al. \\ http://arxiv.org/abs/1905.11600
  29. Graphite: Iterative Generative Modeling of Graphs (2019) Grover A, Zweig A, et al. \\ http://arxiv.org/abs/1803.10459
  30. Junction Tree Variational Autoencoder for Molecular Graph Generation (2019) Jin W, Barzilay R, et al. \\ http://arxiv.org/abs/1802.04364
  31. Knowledge-Embedded Routing Network for Scene Graph Generation (2019) Chen T, Yu W, et al. \\ http://arxiv.org/abs/1903.03326
  32. Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (2019) You J, Liu B, et al. \\ http://arxiv.org/abs/1806.02473
  33. Efficient Graph Generation with Graph Recurrent Attention Networks (2019) Liao R, Li Y, et al. \\ http://papers.nips.cc/paper/8678-efficient-graph-generation-with-graph-recurrent-attention-networks.pdf
  34. Learning to Compose Dynamic Tree Structures for Visual Contexts (2018) Tang K, Zhang H, et al. \\ http://arxiv.org/abs/1812.01880
  35. Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoning (2018) Gay P, James S, et al. \\ http://arxiv.org/abs/1807.05933
  36. Aesthetic Discrimination of Graph Layouts (2018) Klammler M, Mchedlidze T, et al. \\ http://arxiv.org/abs/1809.01017
  37. Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing (2018) Chen B, Sun L, et al. \\ http://arxiv.org/abs/1809.00773
  38. Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation (2018) Li Y, Ouyang W, et al. \\ http://arxiv.org/abs/1806.11538
  39. Graph R-CNN for Scene Graph Generation (2018) Yang J, Lu J, et al. \\ http://arxiv.org/abs/1808.00191
  40. GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models (2018) You J, Ying R, et al. \\ http://arxiv.org/abs/1802.08773
  41. NetGAN: Generating Graphs via Random Walks (2018) Bojchevski A, Shchur O, et al. \\ http://arxiv.org/abs/1803.00816
  42. MolGAN: An implicit generative model for small molecular graphs (2018) De Cao N, Kipf T. \\ http://arxiv.org/abs/1805.11973
  43. Pixels to Graphs by Associative Embedding (2018) Newell A, Deng J. \\ http://arxiv.org/abs/1706.07365
  44. Learning Deep Generative Models of Graphs (2018) Li Y, Vinyals O, et al. \\ http://arxiv.org/abs/1803.03324
  45. GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders (2018) Simonovsky M, Komodakis N. \\ http://arxiv.org/abs/1802.03480
  46. Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders (2018) Ma T, Chen J, et al. \\ http://papers.nips.cc/paper/7942-constrained-generation-of-semantically-valid-graphs-via-regularizing-variational-autoencoders.pdf
  47. Scene Graph Generation from Objects, Phrases and Region Captions (2017) Li Y, Ouyang W, et al. \\ http://arxiv.org/abs/1707.09700
  48. Node Embedding via Word Embedding for Network Community Discovery (2017) Ding W, Lin C, et al. \\ http://arxiv.org/abs/1611.03028
  49. Scene Graph Generation by Iterative Message Passing (2017) Xu D, Zhu Y, et al. \\ http://arxiv.org/abs/1701.02426
  50. Learning graphical state transitions (2017) Johnson DD. \\ https://openreview.net/forum?id=HJ0NvFzxl
  51. Variational Graph Auto-Encoders (2016) Kipf TN, Welling M. \\ http://arxiv.org/abs/1611.07308
  52. Graphs over time: densification laws, shrinking diameters and possible explanations (2005) Leskovec J, Kleinberg J, et al. \\ http://portal.acm.org/citation.cfm?doid=1081870.1081893 \\ doi: 10.1145/1081870.1081893

Spatio-Temporal Graph

  1. Spatio-Temporal Graph Routing for Skeleton-Based Action Recognition (2019) Li B, Li X, et al. \\ doi: 10.1609/aaai.v33i01.33018561
  2. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (2019) Guo S, Lin Y, et al. \\ doi: 10.1609/aaai.v33i01.3301922
  3. Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting (2019) Geng X, Li Y, et al. \\ doi: 10.1609/aaai.v33i01.33013656
  4. Graph WaveNet for Deep Spatial-Temporal Graph Modeling (2019) Wu Z, Pan S, et al. \\ http://arxiv.org/abs/1906.00121
  5. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting (2018) Yu B, Yin H, et al. \\ doi: 10.24963/ijcai.2018/505
  6. Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction (2018) Yao H, Wu F, et al. \\ http://arxiv.org/abs/1802.08714
  7. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting (2018) Li Y, Yu R, et al. \\ http://arxiv.org/abs/1707.01926
  8. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition (2018) Yan S, Xiong Y, et al. \\ http://arxiv.org/abs/1801.07455
  9. Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs (2017) Trivedi R, Dai H, et al. \\ http://arxiv.org/abs/1705.05742
  10. Structured Sequence Modeling with Graph Convolutional Recurrent Networks (2016) Seo Y, Defferrard M, et al. \\ http://arxiv.org/abs/1612.07659
  11. Structural-RNN: Deep Learning on Spatio-Temporal Graphs (2016) Jain A, Zamir AR, et al. \\ http://arxiv.org/abs/1511.05298

Issues

Why should I create this warehouse ?

I decided to study in this field. I have also organized a lot of papers on graphs daily. I share them, mainly for these two reasons:

  1. Hope to promote academic exchanges
  2. A little help for everyone

Missed a paper ?

At present, I am the only one maintaining it.. Even in the field of graph neural network, there are many conferences and journals every month/year. With limited energy, there is no way to cover the entire field. If I missed an paper, please feel free to let me know.

This paper is wrongly classified ?

I am sorry for this situation, please feel free to let me know.

An paper matches multiple categories ？

I am also trying more classification methods, such as labeling boxes. Although it seems simple, it will increase my workload. So if you cannot find the paper you want under one category, you can look at more categories.