- Demo: AliGraph link
- Workshop: Alibaba Group link
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 |
| 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 |
| 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 |
| 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 |
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 |
| 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 |
License
To the extent possible under law, Hongwei Jin has waived all copyright and related or neighboring rights to this work.
