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Deep Learning

2020

  • Principal Neighbourhood Aggregation for Graph Nets (ArXiV 2020)

  • ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations (AAAI 2020)

  • PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures (AISTATS 2020)

  • Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks (ArXiv 2020)

    • Muhammet Balcilar, Guillaume Renton, Pierre Heroux, Benoit Gauzere, Sebastien Adam, Paul Honeine
    • [Paper]
    • [Python Reference]

  • Segmented Graph-Bert for Graph Instance Modeling (ArXiv 2020)

  • Deep Graph Mapper: Seeing Graphs through the Neural Lens (ArXiv 2020)

  • Benchmarking Graph Neural Networks (ArXiv 2020)

  • Building Attention and Edge Convolution Neural Networks for Bioactivity and Physical-Chemical Property Prediction (BiorXiv 2020)

  • Second-Order Pooling for Graph Neural Networks (IEEE Transactions on Pattern Analysis and Machine Intelligence 2020)

  • Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport (ICLR 2020)

  • IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification (ICLR 2020)

  • Few-shot Learning on Graphs Via Super-Classes Based on Graph Spectral Measures (ICLR 2020)

  • Memory-Based Graph Networks (ICLR 2020)

  • A Fair Comparison of Graph Neural Networks for Graph Classification (ICLR 2020)

  • StructPool: Structured Graph Pooling via Conditional Random Fields (ICLR 2020)

  • Strategies for Pre-training Graph Neural Networks (ICLR 2020)

  • InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization (ICLR 2020)

  • Convolutional Kernel Networks for Graph-Structured Data (ICML 2020)

  • Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation (IJCAI 2020)

  • Mutual Information Maximization in Graph Neural Networks (IJCNN 2020)

2019

  • GSSNN: Graph Smoothing Splines Neural Network (AAAI 2020)

  • Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (AAAI 2019)

    • Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe
    • [Paper]
    • [Python Reference]

  • DAGCN: Dual Attention Graph Convolutional Networks (ACPR 2019)

  • Understanding Isomorphism Bias in Graph Data Sets (Arxiv 2019)

  • Graph Star Net for Generalized Multi-Task Learning (Arxiv 2019)

  • HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction (Arxiv 2019)

  • Spectral Clustering with Graph Neural Networks for Graph Pooling (Arxiv 2019)

  • Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling (Arxiv 2019)

  • Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations (Arxiv 2019)

  • Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification (Arxiv 2019)

  • Universal Self-Attention Network for Graph Classification (Arxiv 2019)

  • Discriminative Structural Graph Classification (ArXiV 2019)

  • Symmetrical Graph Neural Network for Quantum Chemistry, with Dual R/K Space (ArXiV 2019)

  • Graph Classification with Automatic Topologically-Oriented Learning (ArXiV 2019)

  • Unsupervised Universal Self-Attention Network for Graph Classification (Arxiv 2019)

  • Crystal Graph Neural Networks for Data Mining in Materials Science (Arxiv 2019)

  • Fast Training of Sparse Graph Neural Networks on Dense Hardware (Arxiv 2019)

  • Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling (Arxiv 2019)

  • Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification (Arxiv 2019)

  • K-hop Graph Neural Networks (Arxiv 2019)

  • Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification (ECML-PKDD 2019)

  • AttPool: Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention Mechanism (ICCV 2019)

  • Variational Recurrent Neural Networks for Graph Classification (ICLR RLGM 2019)

  • edGNN: a Simple and Powerful GNN for Directed Labeled Graphs (ICLR RLGM 2019)

    • Guillaume Jaume, An-phi Nguyen, María Rodríguez Martínez, Jean-Philippe Thiran, Maria Gabrani
    • [Paper]
    • [Python Reference]

  • Capsule Graph Neural Network (ICLR 2019)

  • How Powerful are Graph Neural Networks? (ICLR 2019)

  • Graph U-Nets (ICML 2019)

  • Relational Pooling for Graph Representations (ICML 2019)

  • IPC: A Benchmark Data Set for Learning with Graph-Structured Data (ICML LRGSD 2019)

  • Ego-CNN: Distributed, Egocentric Representations of Graphs for Detecting Critical Structure (ICML 2019)

  • Self-Attention Graph Pooling (ICML 2019)

  • Explainability Techniques for Graph Convolutional Networks (ICML 2019 Workshop)

  • Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity (IJCAI 2019)

  • Molecule Property Prediction Based on Spatial Graph Embedding (Journal of Cheminformatics Models 2019)

  • Graph Convolutional Networks with EigenPooling (KDD 2019)

  • Distance Metric Learning for Graph Structured Data (KDD 2019)

  • Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels (NeurIPS 2019)

  • Provably Powerful Graph Networks (NeurIPS 2019)

  • Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction (NeurIPS 2019)

  • Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019)

2018

  • An End-to-End Deep Learning Architecture for Graph Classification (AAAI 2018)

  • Three-Dimensionally Embedded Graph Convolutional Network for Molecule Interpretation (Arxiv 2018)

  • Learning Graph-Level Representations with Recurrent Neural Networks (Arxiv 2018)

  • Deeply Learning Molecular Structure-Property Relationships Using Graph Attention Neural Network (ArXiv 2018)

  • Edge Attention-based Multi-Relational Graph Convolutional Networks (ArXiv 2018)

  • Compound-Protein Interaction Prediction with End-to-end Learning of Neural Networks for Graphs and Sequences (Bioinformatics 2018)

  • Machine Learning for Organic Cage Property Prediction (Chemical Matters 2018)

  • Kernel Graph Convolutional Neural Networks (ICANN 2018)

    • Giannis Nikolentzos, Polykarpos Meladianos, Antoine Jean-Pierre Tixier, Konstantinos Skianis, Michalis Vazirgiannis
    • [Paper]
    • [Python Reference]

  • Residual Gated Graph ConvNets (ICLR 2018)

  • Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing (ICML 2018)

  • MolGAN: An Implicit Generative Model for Small Molecular Graphs (ICML 2018)

  • Graph Capsule Convolutional Neural Networks (ICML 2018)

  • Learning Graph Distances with Message Passing Neural Networks (ICPR 2018)

  • Commonsense Knowledge Aware Conversation Generation with Graph Attention (IJCAI-ECAI 2018)

  • SGR: Self-Supervised Spectral Graph Representation Learning (KDD DLDay 2018)

  • Graph Classification Using Structural Attention (KDD 2018)

  • Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (NIPS 2018)

  • Hierarchical Graph Representation Learning with Differentiable Pooling (NIPS 2018)

  • Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks (The Journal of Physical Chemistry Letters 2018)

2017

  • Semi-Supervised Learning of Hierarchical Representations of Molecules Using Neural Message Passing (ArXiv 2017)

  • Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (CVPR 2017)

  • Graph Classification with 2D Convolutional Neural Networks (ICANN 2019)

  • Deriving Neural Architectures from Sequence and Graph Kernels (ICML 2017)

  • CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters (IEEE TSP 2017)

  • Deep Learning with Topological Signatures (NIPS 2017)

  • Protein Interface Prediction using Graph Convolutional Networks (NIPS 2017)

2016