gnn_in_neurips_2019
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cshjin
A comprehensive collection of GNN works in NeurIPS 2019.
GNNs in NeurIPS 2019
Sunday (Expo day)
Monday (workshop and tutorial)
| title | topic | session |
|---|---|---|
| Edge Contraction Pooling for Graph Neural Networks | graph pooling | NewInML |
| Popularity Agnostic Evaluation of Knowledge Graph Embeddings | knowledge graph | NewInML |
| Triplet-Aware Scene Graph Embeddings | graph embedding | WiML |
| Applying Graph Neural Networks on Multimodal Biological Data | GNN | WiML |
| Graph combinatorics based group-level network inference with an application to brain connectome study | graph embedding | WiML |
| Predictive Temporal Embedding of Dynamic Graphs | graph embedding | WiML |
| Knowledge Hypergraphs: Extending Knowledge Graphs Beyond Binary Relations | knowledge graph | WiML |
| Construction of knowledge graphs from Spanish text using Linked Data | knowledge graph | WiML |
| Community Detection with Graph Convolutional Networks using Semi-supervised Node Classification | GCN | WiML |
| Robust representations for transfer learning on heterogeneous spatial graphs Chidubem Iddianozie | spatial graph | BAI |
| Machine Learning for Computational Biology and Health | general | Tutorial |
Tuesday (main track)
Posters
| title | session | poster |
|---|---|---|
| Certifiable Robustness to Graph Perturbations | adversarial learning | link |
| Spectral Modification of Graphs for Improved Spectral Clustering | clustering | link |
| Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs | representation learning | link |
| Provably Powerful Graph Networks | representation learning | link |
| Quaternion Knowledge Graph Embeddings | representation learning | link |
| Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy | privacy | link |
| GNNExplainer: Generating Explanations for Graph Neural Networks | deep learning | link |
| Efficient Graph Generation with Graph Recurrent Attention Networks | generative model | link |
| PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph | generative model | link |
| Exact Combinatorial Optimization with Graph Convolutional Neural Networks | combinatorial optimization | link |
| D-VAE: A Variational Autoencoder for Directed Acyclic Graphs | AutoML | link |
| Learning to Propagate for Graph Meta-Learning | meta learning | link |
| Retrosynthesis Prediction with Conditional Graph Logic Network | structure prediction | link |
| Universal Invariant and Equivariant Graph Neural Networks | approximation | link |
Wednesday (main track)
Posters
| title | session | poster |
|---|---|---|
| Heterogeneous Graph Learning for Visual Commonsense Reasoning | representation learning | link spotlight |
| A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning | adverarial learning | link |
| Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks | semi-supervised learning | link |
| Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs | semi-supervised learning | link |
| Graph Agreement Models for Semi-Supervised Learning | semi-supervised learning | link |
| Graph-Based Semi-Supervised Learning with Non-ignorable Non-response | semi-supervised learning | link |
| HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs | semi-supervised learning | link |
| Graph Normalizing Flows | generative model | link |
| Hyper-Graph-Network Decoders for Block Codes | belief propagation | link |
| Structured Graph Learning Via Laplacian Spectral Constraints | graphical model | link |
| Guided Similarity Separation for Image Retrieval | representation learning | link Oral |
| Diffusion Improves Graph Learning | relational learning | link |
| A Flexible Generative Framework for Graph-based Semi-supervised Learning | relational learning | link |
| Online Prediction of Switching Graph Labelings with Cluster Specialists | online learning | link |
| Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels | relational learning | link |
| Hyperbolic Graph Convolutional Neural Networks | relational learning | link |
| Hyperbolic Graph Neural Networks | relational learning | link |
| Multi-relational Poincaré Graph Embeddings | relational learning | link |
| On the equivalence between graph isomorphism testing and function approximation with GNNs | relational learning | link |
| A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening | spectral methods | link |
| Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs | spepctral methods | link |
| Understanding Attention and Generalization in Graph Neural Networks | attention model | link |
| Semi-Implicit Graph Variational Auto-Encoders | variational inference | link |
Thursday (main track)
Posters
| title | session | poster |
|---|---|---|
| Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks | representation learning | link |
| Learning Transferable Graph Exploration | application | link |
| KerGM: Kernelized Graph Matching | kernel method | link spotlight |
| N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules | representation learning | link spotlight |
| Rethinking Kernel Methods for Node Representation Learning on Graphs | kernel method | link |
| Graph Transformer Networks | representation learning | link |
| Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology | representation learning | link |
| Exploring Algorithmic Fairness in Robust Graph Covering Problem | fairness | link |
| Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks | privacy | link |
| On Differentially Private Graph Sparsification and Applications | privacy | link |
| DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters | convolutional filter | link |
| Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks | representation learning | link |
| Wasserstein Weisfeiler-Lehman Graph Kernels | kernel method | link spotlight |
| Learning metrics for persistence-based summaries and applications for graph classification | kernel method | link |
| Generative Models for Graph-Based Protein Design | generative model | link |
| Graph Structured Prediction Energy Networks | structure prediction | link |
| Conditional Structure Generation through Graph Variational Generative Adversarial Nets | graph embedding | link |
| GOT: An Optimal Transport framework for Graph comparison | network analysis | link |
| Variational Graph Recurrent Neural Networks | network analysis | link |
| vGraph: A Generative Model for Joint Community Detection and Node Representation Learning | representation learning | link |
| Learning Transferable Graph Exploration | graph embedding | link |
| Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks | generative model | link |
| Recurrent Space-time Graph Neural Networks | representation learning | link |
| End to end learning and optimization on graphs | combinatorial optimization | link |
| DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs | representation learning | link |
Friday (workshop)
Workshop Graph Representation Learning @ West Exhibition Hall A
Covered in other workshops
| title | topic | workshop |
|---|---|---|
| Probabilistic End-to-End Graph-based Semi-Supervised Learning | semi-supervised learning | BDL |
| Entropic Graph Spectrum | clustering | ITML |
| Deep Clustering by Gaussian Mixture Variational Autoencoders with Graph Embedding | clustering | ITML |
| Graph Structured Prediction Energy Net Algorithms | structure prediction | PGR |
| Learning Optimization Models of Graphs | optimization | PGR |
| Structured differentiable models of 3D scenes via generative scene graphs | generative model | PGR |
| Populating Web Scale Knowledge Graphs using Distantly Supervised Relation Extraction and Validation | knowledge graph | KR2ML |
| Can Graph Neural Networks Help Logic Reasoning? | knowledge graph | KR2ML |
| Knowledge Graph-Driven Conversational Agents | knowledge graph | KR2ML |
| TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces | knowledge graph | KR2ML |
Saturday (workshop)
| title | topic | workshop |
|---|---|---|
| Generalization Bounds for Knowledge Graph Embedding (Trained by Maximum Likelihood) | graph embedding | ML with Guarantees |
| Functional Annotation of Human Cognitive States using Graph Convolution Networks | representation learning | Neuro AI workshop (contributed talk) |
| Learning Symbolic Physics with Graph Networks | physics | ML4Physics |
| SwarmNet: Towards Imitation Learning of Multi-Robot Behavior with Graph Neural Networks | application | robot-learning |
| A Knowledge Graph Based Health Assistant | knowledge graph | AISG |
| Zero-Shot Learning for Fast Optimization of Computation Graphs | optimization | ML for system |
| Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering | knowledge graph | conversational AI |
| The Graph Hawkes Network for Reasoning on Temporal Knowledge Graphs | knowledge graph | TPP (oral) |
| Deep Hyperedges: a Framework for Transductive and Inductive Learning on Hypergraphs | representation learning | sets partitions |
| Finding densest subgraph in probabilistically evolving graphs | structural learning | sets partitions |
| Hypergraph Partitioning using Tensor Eigenvalue Decomposition | structural learning | sets partitions |
| Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networks | application | ML4AD |
| Efficient structure learning with automatic sparsity selection for causal graph processes | causal inference | causal ML |
| A Graph Autoencoder Approach to Causal Structure Learning | structural learning | causal ML |
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To the extent possible under law, Hongwei Jin has waived all copyright and related or neighboring rights to this work.
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