GNN Paper

Review

• Graph Neural Networks: A Review of Methods and Applications, 2018

• Deep Learning on Graphs: A Survey, 2018

• Relationanl Inductive Biases, Deep Learning, and Graph Neural Networks, 2018

• Geometric Deep Learning: Going beyond Euclidean data, 2017

• Computational Capabilities of Graph Neural Networks, 2009

• Neural Message Passing for Quantum Chemistry, 2017

• Non-local Neural Networks, 2018

• The Graph Neural Network Model, 2009

Model

• A new model for learning in graph domains, 2005

• Graph Neural Networks for Ranking Web Pages, 2005

• Gated Graph Sequence Neural Networks, 2015

• Geometric deep learning on graphs and manifolds using mixture model CNNs, 2016

• Spectral Networks and locally Connected Networks on Graphs, 2013

• Deep Convolutional Networks on Graph-Structure Data, 2015

• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, 2016

• Learning Convolutional Neural Networks for Graphs, 2016

• Semi-Supervised Classification with Graph Convolutional Networks, 2016

• Graph Attention Networks, 2017

• Deep Sets, 2017

• Graph Partition Neural Networks for Semi-Supervised Classification, 2018

• Covariant Compositional Networks For Learning Graphs, 2018

• Modeling Relational Data with Graph Convolutional Networks, 2018

• Stochastic Training of Graph Convolutional Networks with Variance Reduction, 2018

• Learning Steady-States of Iterative Algorithms over Graphs, 2018

• Deriving Neural Architectures from Sequence and Graph Kernels, 2017

• Adaptive Graph Convolutional Neural Networks, 2018

• Graph-to-Sequence Learning using Gated Graph Neural Networks, 2018

• Deeper insights into Graph Convolutional Networks for Semi-Supervised Learning, 2018

• Graphical-Based Learning Environments for Pattern Recognition, 2004

• A Comparison between Recursive Neural Networks and Graph Neural Networks, 2006

• Graph Neural Networks for Object Localization, 2006

• Knowledge-Guided Recurrent Neural Network Learning for Task-Oriented Action Prediction, 2017

• Semantic Object Parsing with Graph LSTM, 2016

• CelebrityNet: A Social Network Constructed from Large-Scale Online Celebrity Images, 2015

• Inductive Representation Learning on Large Graphs, 2017

• Graph Classification using Structural Attention, 2018

• Adversarial Attacks on Neural Networks for Graph Data, 2018

• Large-Scale Learnable Graph Convolutional Networks, 2018

• Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing, 2018

• Diffusion-Convolutional Neural Networks, 2016

• Neural networks for relational learning: an experimental comparison, 2011

• FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling, 2018

• Adaptive Sampling Towards Fast Graph Representation Learning, 2018

Application

• Discovering objects and their relations from entanglrd scene representations, 2017

• A simple neural network module for relational reasoning, 2017

• Attend, Infer, Repeat: Fast Scene Understanding with Generative Models

• Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships

• Understanding Kin Relationships in a Photo

• Graph-Structured Representations for Visual Question Answering

• Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

• Few-Shot Learning with Graph Neural Networks

• The More You Know: Using Knowledge Graphs for Image Classification

• Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs

• Rethinking Knowledge Graph Propagation for Zero-Shot Learning

• Interaction Networks for Learning about Objects, Relations and Physics

• A Compositional Object-Based Approach to Learning Physical Dynamics

• Visual Interaction Networks: Learning a Physics Simulator from Video

• Relational neural expectation maximization: Unsupervised discovery of objects and their interactions

• Graph networks as learnable physics engines for inference and control

• Learning Multiagent Communication with Backpropagation

• VAIN: Attentional Multi-agent Predictive Modeling

• Neural Relational Inference for Interacting Systems

• Translating Embeddings for Modeling Multi-relational Data