# Awesome-Graph-Papers

**Repository Path**: chengsen/Awesome-Graph-Papers

## Basic Information

- **Project Name**: Awesome-Graph-Papers
- **Description**: 收集关于图的论文
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-07-16
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

**Awesome Graph Papers**

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

Powerer by Zotero

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

- [2020 Top Conference](#2020-top-conference)
- [Survey](#survey)
- [Graph Generation](#graph-generation)
- [Spatio-Temporal Graph](#spatio-temporal-graph)


## 2020 Top Conference

* [KDD 2020](/2020/KDD2020.md)
* [ACL 2020](/2020/ACL2020.md)
* [AAAI 2020](/2020/AAAI2020.md)

## Survey

1. Deep Learning on Graphs: A Survey (2020) Zhang Z, Cui P, et al. \\\ http://arxiv.org/abs/1812.04202
2. A Survey on Knowledge Graph-Based Recommender Systems (2020) Guo Q, Zhuang F, et al. \\\ http://arxiv.org/abs/2003.00911
3. A Comprehensive Survey on Graph Neural Networks (2020) Wu Z, Pan S, et al. \\\ doi: 10.1109/TNNLS.2020.2978386
4. Graph Learning Approaches to Recommender Systems: A Review (2020) Wang S, Hu L, et al.
5. Introduction to graph neural networks (2020) Liu Z, Zhou J.
6. Graph Neural Networks: A Review of Methods and Applications (2019) Zhou J, Cui G, et al. \\\ http://arxiv.org/abs/1812.08434
7. Relational inductive biases, deep learning, and graph networks (2018) Battaglia PW, Hamrick JB, et al. \\\ http://arxiv.org/abs/1806.01261
8. Attention Models in Graphs: A Survey (2018) Lee JB, Rossi RA, et al. \\\ http://arxiv.org/abs/1807.07984
9. Geometric deep learning: going beyond Euclidean data (2017) Bronstein MM, Bruna J, et al. \\\ doi: 10.1109/MSP.2017.2693418

## 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