Skip to content
/ graph-networks Public

A list of interesting graph neural networks (GNN) links with a primary interest in recommendations and tensorflow that is continually updated and refined

github.com/yazdotai/graph-networks
272 stars 58 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

yazdotai/graph-networks

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

26 Commits
README.md
README.md
 
 

Repository files navigation

Graph Neural Networks and Recommendations

A list of interesting graph neural networks (GNN) material with a primary interest in recommendations and tensorflow that is continually updated and refined

graph neural networks

TensorFlow Implementations

Articles

Videos

Public Datasets

Movies Recommendation:

Music Recommendation:

Books Recommendation:

Food Recommendation:

Merchandise Recommendation:

Healthcare Recommendation:

Dating Recommendation:

Scholarly Paper Recommendation:

Recommendation Algorithms

Research Papers

Relational Representation Learning

Survey papers

  • Graph Neural Networks: A Review of Methods and Applications. Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun. 2018. paper

  • A Comprehensive Survey on Graph Neural Networks. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu. 2019. paper

  • Deep Learning on Graphs: A Survey. Ziwei Zhang, Peng Cui, Wenwu Zhu. 2018. paper

  • Relational Inductive Biases, Deep Learning, and Graph Networks. Battaglia, Peter W and Hamrick, Jessica B and Bapst, Victor and Sanchez-Gonzalez, Alvaro and Zambaldi, Vinicius and Malinowski, Mateusz and Tacchetti, Andrea and Raposo, David and Santoro, Adam and Faulkner, Ryan and others. 2018. paper

  • Geometric Deep Learning: Going beyond Euclidean data. Bronstein, Michael M and Bruna, Joan and LeCun, Yann and Szlam, Arthur and Vandergheynst, Pierre. IEEE SPM 2017. paper

  • Computational Capabilities of Graph Neural Networks. Scarselli, Franco and Gori, Marco and Tsoi, Ah Chung and Hagenbuchner, Markus and Monfardini, Gabriele. IEEE TNN 2009. paper

  • Neural Message Passing for Quantum Chemistry. Gilmer, Justin and Schoenholz, Samuel S and Riley, Patrick F and Vinyals, Oriol and Dahl, George E. 2017. paper

  • Non-local Neural Networks. Wang, Xiaolong and Girshick, Ross and Gupta, Abhinav and He, Kaiming. CVPR 2018. paper

  • The Graph Neural Network Model. Scarselli, Franco and Gori, Marco and Tsoi, Ah Chung and Hagenbuchner, Markus and Monfardini, Gabriele. IEEE TNN 2009. paper

Models

  • A new model for learning in graph domains. Marco Gori, Gabriele Monfardini, Franco Scarselli. IJCNN 2005. paper

  • Graph Neural Networks for Ranking Web Pages. Franco Scarselli, Sweah Liang Yong, Marco Gori, Markus Hagenbuchner, Ah Chung Tsoi, Marco Maggini. WI 2005. paper

  • Gated Graph Sequence Neural Networks. Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel. ICLR 2016. paper

  • Geometric deep learning on graphs and manifolds using mixture model cnns. Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, Michael M. Bronstein. CVPR 2017. paper

  • Spectral Networks and Locally Connected Networks on Graphs. Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun. ICLR 2014. paper

  • Deep Convolutional Networks on Graph-Structured Data. Mikael Henaff, Joan Bruna, Yann LeCun. 2015. paper

  • Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst. NIPS 2016. paper

  • Learning Convolutional Neural Networks for Graphs. Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov. ICML 2016. paper

  • Semi-Supervised Classification with Graph Convolutional Networks. Thomas N. Kipf, Max Welling. ICLR 2017. paper

  • Graph Attention Networks. Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio. ICLR 2018. paper

  • Deep Sets. Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, Alexander Smola. NIPS 2017. paper

  • Graph Partition Neural Networks for Semi-Supervised Classification. Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard Zemel. 2018. paper

  • Covariant Compositional Networks For Learning Graphs. Risi Kondor, Hy Truong Son, Horace Pan, Brandon Anderson, Shubhendu Trivedi. 2018. paper

  • Modeling Relational Data with Graph Convolutional Networks. Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling. ESWC 2018. paper

  • Stochastic Training of Graph Convolutional Networks with Variance Reduction. Jianfei Chen, Jun Zhu, Le Song. ICML 2018. paper

  • Learning Steady-States of Iterative Algorithms over Graphs. Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song. ICML 2018. paper

  • Deriving Neural Architectures from Sequence and Graph Kernels. Tao Lei, Wengong Jin, Regina Barzilay, Tommi Jaakkola. ICML 2017. paper

  • Adaptive Graph Convolutional Neural Networks. Ruoyu Li, Sheng Wang, Feiyun Zhu, Junzhou Huang. AAAI 2018. paper

  • Graph-to-Sequence Learning using Gated Graph Neural Networks. Daniel Beck, Gholamreza Haffari, Trevor Cohn. ACL 2018. paper

  • Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning. Qimai Li, Zhichao Han, Xiao-Ming Wu. AAAI 2018. paper

  • Graphical-Based Learning Environments for Pattern Recognition. Franco Scarselli, Ah Chung Tsoi, Marco Gori, Markus Hagenbuchner. SSPR/SPR 2004. paper

  • A Comparison between Recursive Neural Networks and Graph Neural Networks. Vincenzo Di Massa, Gabriele Monfardini, Lorenzo Sarti, Franco Scarselli, Marco Maggini, Marco Gori. IJCNN 2006. paper

  • Graph Neural Networks for Object Localization. Gabriele Monfardini, Vincenzo Di Massa, Franco Scarselli, Marco Gori. ECAI 2006. paper

  • Knowledge-Guided Recurrent Neural Network Learning for Task-Oriented Action Prediction. Liang Lin, Lili Huang, Tianshui Chen, Yukang Gan, Hui Cheng. ICME 2017. paper

  • Semantic Object Parsing with Graph LSTM. Xiaodan LiangXiaohui ShenJiashi FengLiang Lin, Shuicheng Yan. ECCV 2016. paper

  • CelebrityNet: A Social Network Constructed from Large-Scale Online Celebrity Images. Li-Jia Li, David A. Shamma, Xiangnan Kong, Sina Jafarpour, Roelof Van Zwol, Xuanhui Wang. TOMM 2015. paper

  • Inductive Representation Learning on Large Graphs. William L. Hamilton, Rex Ying, Jure Leskovec. NIPS 2017. paper

  • Graph Classification using Structural Attention. John Boaz Lee, Ryan Rossi, Xiangnan Kong. KDD 18. paper

  • Adversarial Attacks on Neural Networks for Graph Data. Daniel Zügner, Amir Akbarnejad, Stephan Günnemann. KDD 18. paper

  • Large-Scale Learnable Graph Convolutional Networks. Hongyang Gao, Zhengyang Wang, Shuiwang Ji. KDD 18. paper

  • Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing. Davide Bacciu, Federico Errica, Alessio Micheli. ICML 2018. paper

  • Diffusion-Convolutional Neural Networks. James Atwood, Don Towsley. NIPS 2016. paper

  • Neural networks for relational learning: an experimental comparison. Werner Uwents, Gabriele Monfardini, Hendrik Blockeel, Marco Gori, Franco Scarselli. Machine Learning 2011. paper

  • FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. Jie Chen, Tengfei Ma, Cao Xiao. ICLR 2018. paper

  • Adaptive Sampling Towards Fast Graph Representation Learning. Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang. NeurIPS 2018. paper

  • Structure-Aware Convolutional Neural Networks. Jianlong Chang, Jie Gu, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan. NeurIPS 2018. paper

  • Bayesian Semi-supervised Learning with Graph Gaussian Processes. Yin Cheng Ng, Nicolò Colombo, Ricardo Silva. NeurIPS 2018. paper

  • Mean-field theory of graph neural networks in graph partitioning. Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi. NeurIPS 2018. paper

  • Hierarchical Graph Representation Learning with Differentiable Pooling. Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, Jure Leskovec. NeurIPS 2018. paper

  • How Powerful are Graph Neural Networks? Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka. ICLR 2019. paper

  • Graph Capsule Convolutional Neural Networks. Saurabh Verma, Zhi-Li Zhang. ICML 2018 Workshop. paper

  • Capsule Graph Neural Network. Zhang Xinyi, Lihui Chen. ICLR 2019. paper

Applications

  • Discovering objects and their relations from entangled scene representations. David Raposo, Adam Santoro, David Barrett, Razvan Pascanu, Timothy Lillicrap, Peter Battaglia. ICLR Workshop 2017. paper

  • A simple neural network module for relational reasoning. Adam Santoro, David Raposo, David G.T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap. NIPS 2017. paper

  • Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, Geoffrey E. Hinton. NIPS 2016. paper

  • Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships. Tomasz Malisiewicz, Alyosha Efros. NIPS 2009. paper

  • Understanding Kin Relationships in a Photo. Siyu Xia, Ming Shao, Jiebo Luo, Yun Fu. TMM 2012. paper

  • Graph-Structured Representations for Visual Question Answering. Damien Teney, Lingqiao Liu, Anton van den Hengel. CVPR 2017. paper

  • Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition. Sijie Yan, Yuanjun Xiong, Dahua Lin. AAAI 2018. paper

  • Few-Shot Learning with Graph Neural Networks. Victor Garcia, Joan Bruna. ICLR 2018. paper

  • The More You Know: Using Knowledge Graphs for Image Classification. Kenneth Marino, Ruslan Salakhutdinov, Abhinav Gupta. CVPR 2017. paper

  • Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs. Xiaolong Wang, Yufei Ye, Abhinav Gupta. CVPR 2018. paper

  • Rethinking Knowledge Graph Propagation for Zero-Shot Learning. Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, Eric P. Xing. 2018. paper

  • Interaction Networks for Learning about Objects, Relations and Physics. Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, Koray Kavukcuoglu. NIPS 2016. paper

  • A Compositional Object-Based Approach to Learning Physical Dynamics. Michael B. Chang, Tomer Ullman, Antonio Torralba, Joshua B. Tenenbaum. ICLR 2017. paper

  • Visual Interaction Networks: Learning a Physics Simulator from Vide.o Nicholas Watters, Andrea Tacchetti, Théophane Weber, Razvan Pascanu, Peter Battaglia, Daniel Zoran. NIPS 2017. paper

  • Relational neural expectation maximization: Unsupervised discovery of objects and their interactions. Sjoerd van Steenkiste, Michael Chang, Klaus Greff, Jürgen Schmidhuber. ICLR 2018. paper

  • Graph networks as learnable physics engines for inference and control. Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, Peter Battaglia. ICML 2018. paper

  • Learning Multiagent Communication with Backpropagation. Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus. NIPS 2016. paper

  • VAIN: Attentional Multi-agent Predictive Modeling. Yedid Hoshen. NIPS 2017 paper

  • Neural Relational Inference for Interacting Systems. Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel. ICML 2018. paper

  • Translating Embeddings for Modeling Multi-relational Data. Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko. NIPS 2013. paper

  • Representation learning for visual-relational knowledge graphs. Daniel Oñoro-Rubio, Mathias Niepert, Alberto García-Durán, Roberto González, Roberto J. López-Sastre. 2017. paper

  • Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach. Takuo Hamaguchi, Hidekazu Oiwa, Masashi Shimbo, Yuji Matsumoto. IJCAI 2017. paper

  • Representation Learning on Graphs with Jumping Knowledge Networks. Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka. ICML 2018. paper

  • Multi-Label Zero-Shot Learning with Structured Knowledge Graphs. Chung-Wei Lee, Wei Fang, Chih-Kuan Yeh, Yu-Chiang Frank Wang. CVPR 2018. paper

  • Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams. Daesik Kim, Youngjoon Yoo, Jeesoo Kim, Sangkuk Lee, Nojun Kwak. CVPR 2018. paper

  • Deep Reasoning with Knowledge Graph for Social Relationship Understanding. Zhouxia Wang, Tianshui Chen, Jimmy Ren, Weihao Yu, Hui Cheng, Liang Lin. IJCAI 2018. paper

  • Constructing Narrative Event Evolutionary Graph for Script Event Prediction. Zhongyang Li, Xiao Ding, Ting Liu. IJCAI 2018. paper

  • Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering. Daniil Sorokin, Iryna Gurevych. COLING 2018. paper

  • Convolutional networks on graphs for learning molecular fingerprints. David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams. NIPS 2015. paper

  • Molecular Graph Convolutions: Moving Beyond Fingerprints. Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, Patrick Riley. Journal of computer-aided molecular design 2016. paper

  • Protein Interface Prediction using Graph Convolutional Networks. Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur. NIPS 2017. paper

  • Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. Zhiyong Cui, Kristian Henrickson, Ruimin Ke, Yinhai Wang. 2018. paper

  • Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. Bing Yu, Haoteng Yin, Zhanxing Zhu. IJCAI 2018. paper

  • Semi-supervised User Geolocation via Graph Convolutional Networks. Afshin Rahimi, Trevor Cohn, Timothy Baldwin. ACL 2018. paper

  • Dynamic Graph CNN for Learning on Point Clouds. Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon. CVPR 2018. paper

  • PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas. CVPR 2018. paper

  • 3D Graph Neural Networks for RGBD Semantic Segmentation. Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun. CVPR 2017. paper

  • Iterative Visual Reasoning Beyond Convolutions. Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta. CVPR 2018. paper

  • Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs. Martin Simonovsky, Nikos Komodakis. CVPR 2017. paper

  • Situation Recognition with Graph Neural Networks. Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler. ICCV 2017. paper

  • Conversation Modeling on Reddit using a Graph-Structured LSTM. Vicky Zayats, Mari Ostendorf. TACL 2018. paper

  • Graph Convolutional Networks for Text Classification. Liang Yao, Chengsheng Mao, Yuan Luo. AAAI 2019. paper

  • Attention Is All You Need. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin. NIPS 2017. paper

  • Self-Attention with Relative Position Representations. Peter Shaw, Jakob Uszkoreit, Ashish Vaswani. NAACL 2018. paper

  • Hyperbolic Attention Networks. Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann, Peter Battaglia, Victor Bapst, David Raposo, Adam Santoro, Nando de Freitas 2018. paper

About

A list of interesting graph neural networks (GNN) links with a primary interest in recommendations and tensorflow that is continually updated and refined

github.com/yazdotai/graph-networks

Topics

awesome tensorflow recommendation-system recommender-system graph-neural-networks graph-networks

Resources

Readme
Activity

Stars

272 stars

Watchers

10 watching

Forks

58 forks
Report repository

Releases

No releases published

Packages

No packages published