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Papers Implemented

Graph Representation Learning

Graph Neural Networks

  1. `Convolutional Networks on Graphs for Learning Molecular Fingerprints <NFP_>`_

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

    :class:`NeuralFingerprintConv <torchdrug.layers.NeuralFingerprintConv>`, :class:`NeuralFingerprint <torchdrug.models.NeuralFingerprint>`

  2. `Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering <ChebNet_>`_

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

    :class:`ChebyshevConv <torchdrug.layers.ChebyshevConv>`, :class:`ChebyshevConvolutionalNetwork <torchdrug.models.ChebyshevConvolutionalNetwork>`

  3. `Semi-Supervised Classification with Graph Convolutional Networks <GCN_>`_

    Thomas N. Kipf, Max Welling. ICLR 2017.

    :class:`GraphConv <torchdrug.layers.GraphConv>`, :class:`GraphConvolutionalNetwork <torchdrug.models.GraphConvolutionalNetwork>`

  4. `Neural Message Passing for Quantum Chemistry <ENN-S2S_>`_

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

    :class:`MessagePassing <torchdrug.layers.MessagePassing>`, :class:`MessagePassingNeuralNetwork <torchdrug.models.MessagePassingNeuralNetwork>`

  5. `SchNet: A continuous-filter convolutional neural network for modeling quantum interactions <SchNet_>`_

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

    :class:`ContinuousFilterConv <torchdrug.layers.ContinuousFilterConv>`, :class:`SchNet <torchdrug.models.SchNet>`

  6. `Graph Attention Networks <GAT_>`_

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

    :class:`GraphAttentionConv <torchdrug.layers.GraphAttentionConv>`, :class:`GraphAttentionNetwork <torchdrug.models.GraphAttentionNetwork>`

  7. `Modeling Relational Data with Graph Convolutional Networks <RGCN_>`_

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

    :class:`RelationalGraphConv <torchdrug.layers.RelationalGraphConv>`, :class:`RelationalGraphConvolutionalNetwork <torchdrug.models.RelationalGraphConvolutionalNetwork>`

  8. `How Powerful Are Graph Neural Nerworks? <GIN_>`_

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

    :class:`GraphIsomorphismConv <torchdrug.layers.GraphIsomorphismConv>`, :class:`GraphIsomorphismNetwork <torchdrug.models.GraphIsomorphismNetwork>`

Differentiable Graph Pooling

  1. `Hierarchical Graph Representation Learning with Differentiable Pooling <DiffPool_>`_

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

    :class:`DiffPool <torchdrug.layers.DiffPool>`

  2. `Spectral Clustering with Graph Neural Networks for Graph Pooling <MinCutPool_>`_

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

    :class:`MinCutPool <torchdrug.layers.MinCutPool>`

Readout Layers

  1. `Order Matters: Sequence to sequence for sets <Set2Set_>`_

    Oriol Vinyals, Samy Bengio, Manjunath Kudlur

    :class:`Set2Set <torchdrug.layers.Set2Set>`

Normalization Layers

  1. `PairNorm: Tackling Oversmoothing in GNNs <PairNorm_>`_

    Lingxiao Zhao, Leman Akoglu. ICLR 2020.

    :class:`PairNorm <torchdrug.layers.PairNorm>`

Drug Discovery

Pretrain Molecular Representations

  1. `InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization <InfoGraph_>`_

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

    :class:`InfoGraph <torchdrug.models.InfoGraph>`

  2. `Strategies for Pre-training Graph Neural Networks <AttrMasking_>`_

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

    :class:`EdgePrediction <torchdrug.tasks.EdgePrediction>`, :class:`AttributeMasking <torchdrug.tasks.AttributeMasking>`, :class:`ContextPrediction <torchdrug.tasks.ContextPrediction>`

De Novo Molecule Design

  1. `Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation. <GCPN_>`_

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

    :class:`GCPNGeneration <torchdrug.tasks.GCPNGeneration>`

  2. `GraphAF: A Flow-based Autoregressive Model for Molecular Graph Generation. <GraphAF_>`_

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

    :class:`GraphAutoregressiveFlow <torchdrug.models.GraphAutoregressiveFlow>`, :class:`AutoregressiveGeneration <torchdrug.tasks.AutoregressiveGeneration>`

Retrosynthesis

  1. `A Graph to Graphs Framework for Retrosynthesis Prediction. <G2Gs_>`_

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

    :class:`CenterIdentification <torchdrug.tasks.CenterIdentification>`, :class:`SynthonCompletion <torchdrug.tasks.SynthonCompletion>`, :class:`Retrosynthesis <torchdrug.tasks.Retrosynthesis>`

Knowledge Graph Reasoning

  1. `Translating Embeddings for Modeling Multi-relational Data <TransE_>`_

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

    :func:`transe_score <torchdrug.layers.functional.transe_score>`, :class:`TransE <torchdrug.models.DistMult>`

  2. `Embedding Entities and Relations for Learning and Inference in Knowledge Bases <DistMult_>`_

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

    :func:`distmult_score <torchdrug.layers.functional.distmult_score>`, :class:`DistMult <torchdrug.models.DistMult>`

  3. `Complex Embeddings for Simple Link Prediction <ComplEx_>`_

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

    :func:`complex_score <torchdrug.layers.functional.complex_score>`, :class:`ComplEx <torchdrug.models.DistMult>`

  4. `Differentiable Learning of Logical Rules for Knowledge Base Reasoning <NeuralLP_>`_

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

    :class:`NeuralLogicProgramming <torchdrug.models.NeuralLogicProgramming>`

  5. `SimplE Embedding for Link Prediction in Knowledge Graphs <SimplE_>`_

    Seyed Mehran Kazemi, David Poole. NeurIPS 2018.

    :func:`simple_score <torchdrug.layers.functional.simple_score>`, :class:`SimplE <torchdrug.models.SimplE>`

  6. `RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space <RotatE_>`_

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

    :func:`rotate_score <torchdrug.layers.functional.rotate_score>`, :class:`RotatE <torchdrug.models.RotatE>`

  7. `Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs <KBGAT_>`_

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

    :class:`KnowledgeBaseGraphAttentionNetwork <torchdrug.models.KnowledgeBaseGraphAttentionNetwork>`