This is my paper reading list concerning current interesting graph network problems. Papers are classified by methods and sorted by descending-year orders. The format of each paper follows Title (Journal/Conference/Review_forum Year). Particularly, I will have preference on how graph network can help solve spatial-temporal problems. Because my reading list are very limited, you are highly welcome to help complete the paper reading list.
CONTENT
- graph-network-paper-list
- General review
- Graph Convolution Network (GCN)
- Graph Recurrent Neural Networks
- Graph Embedding and Graph Representation Learning
- Diffusion Graph
- Graph Attention (GAT)
- Graph Kernels
- Generative Graph
- Library
- Interesting groups
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Graph Neural Networks: A Review of Methods and Applications (arXiv 2019)
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Geometric deep learning: going beyond Euclidean data (IEEE Signal Processing Magazine 2017)
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Convolutional Networks on Graphs for Learning Molecular Fingerprints (NeurIPS 2015)
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The graph neural network model (IEEE TRANSACTIONS ON NEURAL NETWORKS 2009)
- Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (NeurIPS 2016)
- Learning Convolutional Neural Networks for Graphs (ICML 2016)
- The emerging field of signal processing on graphs- Extending high-dimensional data analysis to networks and other irregular domains (IEEE Signal Processing Magazine 2013)
- 3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting (arXiv 2019)
- Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (AAAI 2019)
- Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting (arXiv 2019)
- Graph Convolutional Neural Networks for Human Activity Purpose Imputation from GPS-based Trajectory Data (Openreview 2018)
- Spatio-Temporal Graph Convolutional Networks-A Deep Learning Framework for Traffic Forecasting (IJCAI 2017)
- EvolveGCN Evolving Graph Convolutional Networks for Dynamic Graphs (AAAI 2020)
- Dynamic spatial-temporal graph convolutional neural network for Traffic Forecasting(AAAI 2019)
- Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs (arXiv 2019)
- Temporal Link Prediction in Dynamic Networks (MLG Workshop 2019)
- Link Prediction in Dynamic Weighted and Directed Social Network using Supervised Learning (Surface 2015)
- Nonparametric Link Prediction in Dynamic Networks (arXiv 2012)
- Traffic Graph Convolutional Recurrent Neural Network Deep Learning Framework for Network Scale Traffic Learning and Forecasting (arXiv 2019)
- Efficient Metropolitan Traffic Prediction Based on Graph Recurrent Neural Network (arXiv 2018)
- Relational inductive biases, deep learning, and graph networks (arXiv 2018)
- A Comprehensive Survey of Graph Embedding Problems, Techniques and Applications (arXiv 2018)
- Network representation learning: A survey (IEEE transactions on Big Data 2018)
- Representation Learning on Graphs: Methods and Applications (arXiv 2017)
- Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology (NeurIPS 2019)
- Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec (WSDM 2018)
- Anonymous Walk Embeddings (arXiv 2018)
- Inductive Representation Learning on Large Graphs (NeurIPS 2017)
- node2vec: Scalable Feature Learning for Networks (KDD 2016)
- LINE: Large-scale Information Network Embedding (WWW 2015)
- Neural Word Embedding as Implicit Matrix Factorization (NeurIPS 2014)
- DeepWalk: Online Learning of Social Representations (KDD 2014)
- Discriminative embeddings of latent variable models for structured data (ICML 2016)
- Molecular graph convolutions: moving beyond fingerprints (Journal of Computer-Aided Molecular Design 2016)
- Diffusion Improves Graph Learning (NeurIPS 2019)
- Diffusion Convolutional Recurrent Neural Network Data-Driven Traffic Forecasting (arXiv 2018)
- Heterogeneous Graph Attention Network (WWW 2019)
- Relational Graph Attention Networks (ICLR 2019)
- Graph Attention Networks (ICLR 2018)
- DeepInf: Social Influence Prediction with Deep Learning (KDD 2018)
- Inductive Representation Learning on Large Graphs (NeurIPS 2017)
- Neural Message Passing for Quantum Chemistry (arXiv 2017)
- Generative Graph Convolutional Network for Growing Graphs (ICASSP 2019)
- Efficient Graph Generation with Graph Recurrent Attention Networks (NeurIPS 2019)
- A generative graph model for electrical infrastructure networks (Journal of Complex Networks 2018)
- GraphGAN Graph Representation Learning With Generative Adversarial Nets (AAAI 2018)
- Graphite: Iterative Generative Modeling of Graphs (arXiv 2018)
- GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders (ICANN 2018)
- MolGAN: An implicit generative model for small molecular graphs (arXiv 2018)
- GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models (arXiv 2018)
- Junction Tree Variational Autoencoder for Molecular Graph Generation (arXiv 2018)
- Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (NeurIPS 2018)
- Constrained Graph Variational Autoencoders for Molecule Design (NeurIPS 2018)