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

• General review
• Graph Convolution Network (GCN)
  • Fundamental: learn graph in spectral domain
  • Spatial-temporal GCN
  • Dynamic GCN/GNN
  • GCN for Directed Graph
• Graph Recurrent Neural Networks
• Graph Embedding and Graph Representation Learning
  • Survey
  • Embedding nodes
  • Embedding sub-graphs
• Diffusion Graph
• Graph Attention (GAT)
• Graph Kernels
• Generative Graph
• Library
• Interesting groups

General review

• Graph Neural Networks: A Review of Methods and Applications (arXiv 2019)

• How Powerfl are graph neural networks (arXiv 2018)

• Geometric deep learning: going beyond Euclidean data (IEEE Signal Processing Magazine 2017)

• Convolutional Networks on Graphs for Learning Molecular Fingerprints (NeurIPS 2015)

• The graph neural network model (IEEE TRANSACTIONS ON NEURAL NETWORKS 2009)

Graph Convolution Network (GCN)

Fundamental: learn graph in spectral domain

• 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)

  Spatial-temporal GCN

• 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)

Dynamic GCN/GNN

• 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)

  GCN for Directed Graph

• MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS (IEEE Xplore 2018)

Graph Recurrent Neural Networks

• 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)

Graph Embedding and Graph Representation Learning

Survey

• 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)

Embedding nodes

• 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)

Embedding sub-graphs

• 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 Graph

• Diffusion Improves Graph Learning (NeurIPS 2019)
• Diffusion Convolutional Recurrent Neural Network Data-Driven Traffic Forecasting (arXiv 2018)

Graph Attention (GAT)

• 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)

  Graph Kernels

• A survey on graph kernels (arXiv 2019)
• Collective dynamics of ‘small-world’ networks (Nature 1998)

Generative Graph

• 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)

  Library

• Graph Nets library (Deepmind)
• Gated Graph Neural Networks (Microsoft)
• AlphaTree (weslynn)

  Interesting groups

• Jure Leskovec
• William L. Hamilton
• Rose Yu
• Lijun Sun (Our lab!)