## Awesome resources on Graph Neural Networks.
This is a collection of resources related with graph neural networks.
## Contents
- [Survey papers](#surveypapers)
- [Papers](#papers)
- [Recuurent Graph Neural Networks](#rgnn)
- [Convolutional Graph Neural Networks](#cgnn)
- [Graph Autoencoders](#gae)
- [Network Embedding](#ne)
- [Graph Generation](#gg)
- [Spatial-Temporal Graph Neural Networks](#stgnn)
- [Application](#application)
- [Computer Vision](#cv)
- [Natural Language Processing](#nlp)
- [Internet](#web)
- [Recommender Systems](#rec)
- [Healthcare](#health)
- [Chemistry](#chemistry)
- [Physics](#physics)
- [Others](#others)
- [Library](#library)
## Survey papers
1. **A Comprehensive Survey on Graph Neural Networks.** *Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu.* 2019 [paper](https://arxiv.org/pdf/1901.00596.pdf)
1. **Geometric deep learning: going beyond euclidean data.** *Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst.* 2016. [paper](https://arxiv.org/pdf/1611.08097.pdf)
1. **Relational inductive biases, deep learning, and graph networks.**
*Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu.* 2018. [paper](https://arxiv.org/pdf/1806.01261.pdf)
1. **Attention models in graphs.** *John Boaz Lee, Ryan A. Rossi, Sungchul Kim, Nesreen K. Ahmed, Eunyee Koh.* 2018. [paper](https://arxiv.org/pdf/1807.07***4.pdf)
1. **Deep learning on graphs: A survey.** Ziwei Zhang, Peng Cui and Wenwu Zhu. 2018. [paper](https://arxiv.org/pdf/1812.04202.pdf)
1. **Graph Neural Networks: A Review of Methods and Applications** *Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun.* 2018 [paper](https://arxiv.org/pdf/1812.08434.pdf)
## Papers
## Recurrent Graph Neural Networks
1. **Supervised neural networks for the classification of structures** *A. Sperduti and A. Starita.* IEEE Transactions on Neural Networks 1997. [paper](https://www.ncbi.nlm.nih.gov/pubmed/18255672)
1. **A new model for learning in graph domains.** *Marco Gori, Gabriele Monfardini, Franco Scarselli.* IJCNN 2005. [paper](https://ieeexplore.ieee.org/abstract/document/1555942)
1. **The graph neural network model.** *Franco Scarselli,Marco Gori,Ah Chung Tsoi,Markus Hagenbuchner,
Gabriele Monfardini.* 2009. [paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1015.7227&rep=rep1&type=pdf)
1. **Graph echo state networks.** *Claudio Gallicchio, Alessio Micheli* IJCNN 2010. [paper](https://ieeexplore.ieee.org/abstract/document/5596796)
1. **Gated graph sequence neural networks.** *Yujia Li, Richard Zemel, Marc Brockschmidt, Daniel Tarlow.* ICLR 2015. [paper](https://arxiv.org/pdf/1511.05493.pdf)
1. **Learning steady-states of iterative algorithms over graphs.** *Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alexander J. Smola, Le Song* ICML 2018. [paper](http://proceedings.mlr.press/v80/dai18a/dai18a.pdf)
## Convolutional Graph Neural Networks
### Spectral
1. **Spectral networks and locally connected networks on graphs.** *Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun.* ICLR 2014. [paper](https://arxiv.org/pdf/1312.6203.pdf)
1. **Deep convolutional networks on graph-structured data.** *Mikael Henaff, Joan Bruna, Yann LeCun.* 2015. [paper](https://arxiv.org/abs/1506.05163)
1. **Accelerated filtering on graphs using lanczos method.** *Ana Susnjara, Nathanael Perraudin, Daniel Kressner, Pierre Vandergheynst.* 2015. [paper](https://arxiv.org/pdf/1509.04537.pdf)
1. **Convolutional neural networks on graphs with fast localized spectral filtering.** *Michael Defferrard, Xavier Bresson, Pierre Vandergheynst.* NIPS 2016. [paper](https://arxiv.org/pdf/1606.09375.pdf)
1. **Semi-supervised classification with graph convolutional networks.** *Thomas N. Kipf, Max Welling.* ICLR 2017. [paper](https://arxiv.org/pdf/1609.02907.pdf)
1. **Cayleynets: graph convolutional neural networks with complex rational spectral filters.** *Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein.* 2017. [paper](https://arxiv.org/pdf/1705.076***.pdf)
1. **Simplifying Graph Convolutional Networks.** *Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger.* ICML 2019. [paper](https://arxiv.org/pdf/1902.07153.pdf) [code](https://github.com/Tiiiger/SGC)
1. **Graph Wavelet Neural Network.** *Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng.* ICLR 2019. [paper](https://openreview.net/pdf?id=H1ewdiR5tQ)
1. **DIFFUSION SCATTERING TRANSFORMS ON GRAPHS.** *Fernando Gama, Alejandro Ribeiro, Joan Bruna.* ICLR 2019. [paper](https://arxiv.org/pdf/1806.08829.pdf)
### Spatial
1. **Neural network for graphs: A contextual constructive approach.** *A. Micheli.* IEEE Transactions on Neural Networks 2009. [paper](https://ieeexplore.ieee.org/abstract/document/4773279)
1. **Convolutional networks on graphs for learning molecular fingerprints.** *David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre Rafael Go mez-Bombarelli, Timothy Hirzel, Ala n Aspuru-Guzik, Ryan P. Adams.*, NIPS 2015. [paper](http://papers.nips.cc/paper/5954-convolutional-networks-on-graphs-for-learning-molecular-fingerprints.pdf)
1. **Diffusion-convolutional neural networks** *James Atwood, Don Towsley.* NIPS 2016. [paper](https://arxiv.org/pdf/1511.02136.pdf)
1. **Neural message passing for quantum chemistry.** *Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl.* ICML 2017. [paper](https://arxiv.org/pdf/1704.01212.pdf)
1. **Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs** *Martin Simonovsky, Nikos Komodakis* CVPR 2017. [paper](https://arxiv.org/pdf/1704.02901.pdf)
1. **Geometric deep learning on graphs and manifolds using mixture model cnns.** *Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, Michael M. Bronstein.* CVPR 2017. [paper](https://arxiv.org/pdf/1611.08402.pdf)
1. **Robust spatial filtering with graph convolutional neural networks.** 2017. *Felipe Petroski Such, Shagan Sah, Miguel Dominguez, Suhas Pillai, Chao Zhang, Andrew Michael, Nathan Cahill, Raymond Ptucha.* [paper](https://arxiv.org/abs/1703.00792)
1. **Structure-Aware Convolutional Neural Networks.** *Jianlong Chang, Jie Gu, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan.* NeurIPS 2018. [paper](http://papers.nips.cc/paper/7287-structure-aware-convolutional-neural-networks.pdf) [code](https://github.com/vector-1127/SACNNs)
1. **On filter size in graph convolutional network.** *D. V. Tran, A. Sperduti et al.* SSCI. IEEE, 2018. [paper](https://arxiv.org/pdf/1811.10435.pdf)
1. **Predict then Propagate: Graph Neural Networks meet Personalized PageRank.** *Johannes Klicpera, Aleksandar Bojchevski, Stephan Gunnemann.* ICLR 2019. [paper](https://arxiv.org/pdf/1810.05997.pdf) [code](https://github.com/benedekrozemberczki/APPNP)
#### Architecture
1. **Representation learning on graphs with jumping knowledge networks.** *Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka.* ICML 2018. [paper](https://arxiv.org/pdf/1806.03536.pdf)
1. **Dual graph convolutional networks for graph-based semi-supervised classification** *Chenyi Zhuang, Qiang Ma.* WWW 2018. [paper](https://dl.acm.org/citation.cfm?id=3186116)
1. **Graph U-nets** *Hongyang Gao, Shuiwang Ji.* ICML 2019. [paper](https://arxiv.org/pdf/1905.05178.pdf) [code](https://github.com/HongyangGao/gunet)
1. **MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing.** *Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan.* [paper](https://arxiv.org/pdf/1905.00067.pdf) [code](github.com/samihaija/mixhop)
#### Attention/Gating Mechanisms
1. **Graph Attention Networks.**
*Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio.* ICLR 2018. [paper](https://arxiv.org/pdf/1710.10903.pdf) [code](https://github.com/PetarV-/GAT)
1. **Gaan: Gated attention networks for learning on large and spatiotemporal graphs.** *Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-Yan Yeung.* 2018. [paper](https://arxiv.org/pdf/1803.07294.pdf)
1. **Geniepath: Graph neural networks with adaptive receptive paths.** Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi. AAAI 2019. [paper](https://arxiv.org/pdf/1802.00910.pdf)
1. **Graph Representation Learning via Hard and Channel-Wise Attention Networks.** *Hongyang Gao, Shuiwang Ji.* 2019 KDD. [paper](https://www.kdd.org/kdd2019/accepted-papers/view/graph-representation-learning-via-hard-and-channel-wise-attention-networks)
1. **Understanding Attention and Generalization in Graph Neural Networks.** *Boris Knyazev, Graham W. Taylor, Mohamed R. Amer.* NeurIPS 2019. [paper](https://arxiv.org/abs/1905.02850)
#### Convolution
1. **Learning convolutional neural networks for graphs.** *Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov.* ICML 2016. [paper](https://arxiv.org/pdf/1605.05273.pdf)
1. **Large-Scale Learnable Graph Convolutional Networks.** *Hongyang Gao, Zhengyang Wang, Shuiwang Ji.* KDD 2018. [paper](https://arxiv.org/pdf/1808.03965.pdf)
#### Training Methods
1. **Inductive representation learning on large graphs.** *William L. Hamilton, Rex Ying, Jure Leskovec.* NIPS 2017. [paper](http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf)
1. **Stochastic Training of Graph Convolutional Networks with Variance Reduction.**
*Jianfei Chen, Jun Zhu, Le Song.* ICML 2018. [paper](https://arxiv.org/pdf/1710.10568.pdf)
1. **Adaptive Sampling Towards Fast Graph Representation Learning.** *Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang.* NeurIPS 2018. [paper](https://arxiv.org/pdf/1809.05343.pdf) [code]()
1. **FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling.**
*Jie Chen, Tengfei Ma, Cao Xiao.* ICLR 2018. [paper](https://arxiv.org/pdf/1801.10247.pdf)
1. **Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks.** KDD 2019. [paper](https://arxiv.org/pdf/1905.07953.pdf) [code](https://github.com/google-research/google-research/tree/master/cluster_gcn)
#### Pooling
1. **Hierarchical graph representation learning with differentiable pooling.** *Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec.* NeurIPS 2018. [paper](https://arxiv.org/pdf/1806.08804.pdf) [code](https://github.com/RexYing/diffpool)
1. **Self-Attention Graph Pooling.** *Junhyun Lee, Inyeop Lee, Jaewoo Kang.* ICML 2019. [paper](https://arxiv.org/abs/1904.08082) [code](https://github.com/inyeoplee77/SAGPool)
#### Graph Classfication
1. **Contextual graph markov model: A deep and generative approach to graph processing.** *D. Bacciu, F. Errica, A. Micheli.* ICML 2018. [paper](https://arxiv.org/abs/1805.10636)
1. **Adaptive graph convolutional neural networks.** *Ruoyu Li, Sheng Wang, Feiyun Zhu, Junzhou Huang.* AAAI 2018. [paper](https://arxiv.org/pdf/1801.03226.pdf)
1. **Graph capsule convolutional neural networks** *Saurabh Verma, Zhi-Li Zhang.* 2018. [paper](https://arxiv.org/abs/1805.08090)
1. **Capsule Graph Neural Network** *Zhang Xinyi, Lihui Chen.* ICLR 2019. [paper](https://openreview.net/pdf?id=Byl8BnRcYm)
#### Bayesian
1. **Bayesian Semi-supervised Learning with Graph Gaussian Processes
.** *Yin Cheng Ng, Nicolo Colombo, Ricardo Silva* NeurIPS 2018. [paper](https://papers.nips.cc/paper/7440-bayesian-semi-supervised-learning-with-graph-gaussian-processes.pdf)
> It redefines the kernel function in Gaussian Process with graph structure information.
1. **Bayesian Graph Convolutional Neural Networks for Semi-supervised Classification** *Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Ustebay.* AAAI 2019. [paper] (https://arxiv.org/pdf/1811.11103.pdf)
#### Analysis
1. **Deeper insights into graph convolutional networks for semi-supervised learning.** *Qimai Li, Zhichao Han, Xiao-Ming Wu.* AAAI 2018. [paper](https://arxiv.org/pdf/1801.07606.pdf)
1. **How powerful are graph neural networks?** *Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka.* ICLR 2019. [paper](https://arxiv.org/pdf/1810.00826.pdf)
1. **Can GCNs Go as Deep as CNNs?.** *Guohao Li, Matthias Muller, Ali Thabet, Bernard Ghanem.* 2019. ICCV 2019. [paper](https://arxiv.org/abs/1904.03751)
1. **Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks.** *Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, Martin Grohe* AAAI 2019. [paper](https://arxiv.org/pdf/1810.02244.pdf)
#### Miscellaneous Graphs
1. **Modeling relational data with graph convolutional networks** *Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling.* ESW 2018. [paper](https://arxiv.org/pdf/1703.06103.pdf)
1. **Signed graph convolutional network**. *Tyler Derr, Yao Ma, Jiliang Tang.* 2018. [paper](https://arxiv.org/pdf/1808.06354.pdf)
1. **Multidimensional graph convolutional networks** *Yao Ma, Suhang Wang, Charu C. Aggarwal, Dawei Yin, Jiliang Tang.* 2018. [paper](https://arxiv.org/pdf/1808.06099.pdf)
1. **LanczosNet: Multi-Scale Deep Graph Convolutional Networks** *Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel.* ICLR 2019. [paper](https://openreview.net/pdf?id=BkedznAqKQ)
1. **Hypergraph Neural Networks.** *Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao* AAAI 2019. [paper](https://arxiv.org/pdf/1809.09401.pdf)
1. **HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs.** *
Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar.* NeurIPS 2019. [paper](https://arxiv.org/abs/1809.02589)
## Graph Auto-encoder
### Network Embedding
1. **Structural deep network embedding** *Daixin Wang, Peng Cui, Wenwu Zhu.* [paper](https://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdf)
1. **Deep neural networks for learning graph representations.** *Shaosheng Cao, Wei Lu, Qiongkai Xu.* AAAI 2016. [paper](https://www.***i.org/ocs/index.php/AAAI/AAAI16/paper/view/12423/11715)
1. **Variational graph auto-encoders.** *Thomas N. Kipf, Max Welling.* 2016. [paper](https://arxiv.org/pdf/1611.07308.pdf)
1. **Mgae: Marginalized graph autoencoder for graph clustering** *Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang.* CIKM 2017. [paper](https://shiruipan.github.io/pdf/CIKM-17-Wang.pdf)
1. **Link Prediction Based on Graph Neural Networks.** *Muhan Zhang, Yixin Chen.* NeurIPS 2018. [paper](https://arxiv.org/pdf/1802.09691.pdf)
1. **SpectralNet: Spectral Clustering using Deep Neural Networks** *Uri Shaham, Kelly Stanton, Henry Li, Boaz Nadler, Ronen Basri, Yuval Kluger.* ICLR 2018. [paper](https://arxiv.org/pdf/1801.01587.pdf)
1. **Deep Recursive Network Embedding with Regular Equivalence.**
*Ke Tu, Peng Cui, Xiao Wang, Philip S. Yu, Wenwu Zhu.* KDD 2018. [paper](http://cuip.thumedialab.com/papers/NE-RegularEquivalence.pdf)
1. **Learning Deep Network Representations with Adversarially Regularized Autoencoders.**
*Wenchao Yu, Cheng Zheng, Wei Cheng, Charu Aggarwal, Dongjin Song, Bo Zong, Haifeng Chen, Wei Wang.* KDD 2018. [paper](http://www.cs.ucsb.edu/~bzong/doc/kdd-18.pdf)
1. **Adversarially Regularized Graph Autoencoder for Graph Embedding.**
*Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang.* IJCAI 2018. [paper](https://www.ijcai.org/proceedings/2018/0362.pdf)
1. **Deep graph infomax.** *Petar Velickovic, William Fedus, William L. Hamilton, Pietro Lio, Yoshua Bengio, R Devon Hjelm.* ICLR 2019. [paper](https://arxiv.org/abs/1809.10341)
### Graph Generation
1. **Learning graphical state transitions.** *Daniel D. Johnson.* ICLR 2016. [paper](https://openreview.net/pdf?id=HJ0NvFzxl)
1. **MolGAN: An implicit generative model for small molecular graphs.**
*Nicola De Cao, Thomas Kipf.* 2018. [paper](https://arxiv.org/pdf/1805.11973.pdf)
1. **Learning deep generative models of graphs.** *Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia.* ICML 2018. [paper](https://arxiv.org/abs/1803.03324)
1. **Netgan: Generating graphs via random walks.** *Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zugner, Stephan Gunnemann.* ICML 2018. [paper](https://arxiv.org/pdf/1803.00816.pdf)
1. **Graphrnn: A deep generative model for graphs.** *Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec.* ICML 2018. [paper](https://arxiv.org/pdf/1802.08773.pdf)
1. **Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders.** *Tengfei Ma, Jie Chen, Cao Xiao.* NeurIPS 2018. [paper](https://papers.nips.cc/paper/7942-constrained-generation-of-semantically-valid-graphs-via-regularizing-variational-autoencoders.pdf)
1. **Graph convolutional policy network for goal-directed molecular graph generation.** *Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec.* NeurIPS 2018. [paper](https://arxiv.org/abs/1806.02473)
1. **D-VAE: A Variational Autoencoder for Directed Acyclic Graphs.** *Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen.* NeuralIPS 2019. [paper](https://arxiv.org/abs/1904.11088)
## Spatial-Temporal Graph Neural Networks
1. **Structured sequence modeling with graph convolutional recurrent networks.** *Youngjoo Seo, Michael Defferrard, Pierre Vandergheynst, Xavier Bresson.* 2016. [paper](https://arxiv.org/pdf/1612.07659.pdf)
1. **Structural-rnn: Deep learning on spatio-temporal graphs.** *Ashesh Jain, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena.* CVPR 2016. [paper](https://arxiv.org/abs/1511.052***)
1. **Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs.** *
Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song.* ICML 2017 [paper](https://arxiv.org/pdf/1705.05742.pdf)
1. **Deep multi-view spatial-temporal network for taxi.** *Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Zhenhui Li.* AAAI 2018. [paper](https://arxiv.org/abs/1802.08714)
1. **Spatial temporal graph convolutional networks for skeleton-based action recognition.** *Sijie Yan, Yuanjun Xiong, Dahua Lin.* AAAI 2018. [paper](https://arxiv.org/abs/1801.07455)
1. **Diffusion convolutional recurrent neural network: Data-driven traffic forecasting.** *Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu.* ICLR 2018. [paper](https://arxiv.org/pdf/1707.01926.pdf)
1. **Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting.** *Bing Yu, Haoteng Yin, Zhanxing Zhu.* IJCAI 2018. [paper](https://arxiv.org/pdf/1709.04875.pdf)
1. **Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting.** *Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, HuaiyuWan* AAAI 2019. [paper](https://***i.org/ojs/index.php/AAAI/article/view/3881)
1. **Spatio-temporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting.** *Xu Geng, Yaguang Li, Leye ... ...