Papers Implemented

Graph Representation Learning

Graph Neural Networks

  1. Convolutional Networks on Graphs for Learning Molecular Fingerprints

    David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams. NIPS 2015.

    NeuralFingerprintConv, NeuralFingerprint

  2. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

    Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst. NIPS 2016.

    ChebyshevConv, ChebyshevConvolutionalNetwork

  3. Semi-Supervised Classification with Graph Convolutional Networks

    Thomas N. Kipf, Max Welling. ICLR 2017.

    GraphConv, GraphConvolutionalNetwork

  4. Neural Message Passing for Quantum Chemistry

    Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl. ICML 2017.

    MessagePassing, MessagePassingNeuralNetwork

  5. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

    Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller. NeurIPS 2017.

    ContinuousFilterConv, SchNet

  6. Graph Attention Networks

    Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio. ICLR 2018.

    GraphAttentionConv, GraphAttentionNetwork

  7. Modeling Relational Data with Graph Convolutional Networks

    Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling. ESWC 2018.

    RelationalGraphConv, RelationalGraphConvolutionalNetwork

  8. How Powerful Are Graph Neural Nerworks?

    Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka. ICLR 2019.

    GraphIsomorphismConv, GraphIsomorphismNetwork

Differentiable Graph Pooling

  1. Hierarchical Graph Representation Learning with Differentiable Pooling

    Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec. NeurIPS 2018.

    DiffPool

  2. Spectral Clustering with Graph Neural Networks for Graph Pooling

    Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi. ICML 2020.

    MinCutPool

Readout Layers

  1. Order Matters: Sequence to sequence for sets

    Oriol Vinyals, Samy Bengio, Manjunath Kudlur

    Set2Set

Normalization Layers

  1. PairNorm: Tackling Oversmoothing in GNNs

    Lingxiao Zhao, Leman Akoglu. ICLR 2020.

    PairNorm

Drug Discovery

Pretrain Molecular Representations

  1. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

    Fan-Yun Sun, Jordan Hoffman, Vikas Verma, Jian Tang. ICLR 2020.

    InfoGraph

  2. Strategies for Pre-training Graph Neural Networks

    Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec. ICLR 2020.

    EdgePrediction, AttributeMasking, ContextPrediction

De Novo Molecule Design

  1. Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation.

    Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec. NeurIPS 2018.

    GCPNGeneration

  2. GraphAF: A Flow-based Autoregressive Model for Molecular Graph Generation.

    Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang. ICLR 2020.

    GraphAutoregressiveFlow, AutoregressiveGeneration

Retrosynthesis

  1. A Graph to Graphs Framework for Retrosynthesis Prediction.

    Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang. ICML 2020.

    CenterIdentification, SynthonCompletion, Retrosynthesis

Knowledge Graph Reasoning

  1. Translating Embeddings for Modeling Multi-relational Data

    Antoine Bordes, Nicolas Usunier, Alberto García-Durán. NIPS 2013.

    transe_score, TransE

  2. Embedding Entities and Relations for Learning and Inference in Knowledge Bases

    Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng. ICLR 2015.

    distmult_score, DistMult

  3. Complex Embeddings for Simple Link Prediction

    Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard. ICML 2016.

    complex_score, ComplEx

  4. Differentiable Learning of Logical Rules for Knowledge Base Reasoning

    Fan Yang, Zhilin Yang, William W. Cohen. NIPS 2017.

    NeuralLogicProgramming

  5. SimplE Embedding for Link Prediction in Knowledge Graphs

    Seyed Mehran Kazemi, David Poole. NeurIPS 2018.

    simple_score, SimplE

  6. RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

    Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang. ICLR 2019.

    rotate_score, RotatE

  7. Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

    Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul. ACL 2019.

    KnowledgeBaseGraphAttentionNetwork