yazdotai/graph-networks yazdotai/graph-networks

  • Stars
    star
    261
  • Rank 156,630 (Top 4 %)
  • Language
  • Created almost 6 years ago
  • Updated almost 6 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
yazdotai/graph-networks
github

yazdotai

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

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

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

More Repositories

1

machine-learning-video-courses

Comprehensive list of machine learning videos
36
star
2

struct

erlang module to deal with struct created my mochijson2
Erlang
6
star
3

paper-code-data

Machine learning papers with related code and data for easy reference
5
star
4

randomstring

generates random string in erlang
4
star
5

top-machine-learning

top machine learning repos on github
1
star