Graph Neural Network(GNN)最全资源整理分享

2019 年 7 月 9 日 深度学习与NLP

    GNN自去年起,一直是研究的热点,图神经网络相关的关键词频繁出现在今年各大AI顶会论文title中,加深对这一领域的了解是非常必要的。分享一篇,关于GNN,目前看到的整理得最细致的资源列表。

    内容涉及节点表示学习、知识图谱表示学习、图神经网络介绍、图神经网络应用、图生成以及可视化相关的最新论文列,还收集了目前流行的开源GNN平台。

    本文内容整理自网络,源地址:https://github.com/DeepGraphLearning/LiteratureDL4Graph

目录

1.节点表示学习

            1.1无监督节点表示学习

            1.2异构图中的节点表示学习

            1.3动态图中的节点表示学习

  2.知识图谱Embedding化

        3.图神经网络

        4.图神经网络的应用

            4.1自然语言处理

            4.2计算机视觉

            4.3推荐系统

            4.4链接预测

            4.5影响力预测

            4.6神经架构搜索

            4.7强化学习

            4.8组合优化

            4.9对抗性攻击

            4.10元学习

            4.11结构学习

            4.12生物信息学和化学

            4.13定理证明

        5.图生成

        6.图形布局和高维数据可视化

        7.图表示学习系统


资源列表

  1   节点表示学习

  1.1   无监督节点表示学习

    DeepWalk: Online Learning of Social Representations

    Bryan Perozzi, Rami Al-Rfou, Steven Skiena

    KDD 2014


    Node classification, Random walk, Skip-gram

    LINE: Large-scale Information Network Embedding

    Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei

    WWW 2015


    First-order, Second-order, Node classification

    GraRep: Learning Graph Representations with Global Structural Information

    Shaosheng Cao, Wei Lu, Qiongkai Xu

    CIKM 2015


    High-order, SVD

    node2vec: Scalable Feature Learning for Networks

    Aditya Grover, Jure Leskovec

    KDD 2016


    Breadth-first Search, Depth-first Search, Node Classification, Link Prediction

    Variational Graph Auto-Encoders

    Thomas N. Kipf, Max Welling

    arXiv 1611

    Scalable Graph Embedding for Asymmetric Proximity

    Chang Zhou, Yuqiong Liu, Xiaofei Liu, Zhongyi Liu, Jun Gao

    AAAI 2017


    Fast Network Embedding Enhancement via High Order Proximity Approximation

    Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu

    IJCAI 2017


    struc2vec: Learning Node Representations from Structural Identity

    Leonardo F. R. Ribeiro, Pedro H. P. Savarese, Daniel R. Figueiredo

    KDD 2017


    Structural Identity

    Poincaré Embeddings for Learning Hierarchical Representations

    Maximilian Nickel, Douwe Kiela

    NIPS 2017


    VERSE: Versatile Graph Embeddings from Similarity Measures

    Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller

    WWW 2018


    Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec

    Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang

    WSDM 2018


    Learning Structural Node Embeddings via Diffusion Wavelets

    Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec

    KDD 2018


    Adversarial Network Embedding

    Quanyu Dai, Qiang Li, Jian Tang, Dan Wang

    AAAI 2018


    GraphGAN: Graph Representation Learning with Generative Adversarial Nets

    Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo

    AAAI 2018


    A General View for Network Embedding as Matrix Factorization

    Xin Liu, Tsuyoshi Murata, Kyoung-Sook Kim, Chatchawan Kotarasu, Chenyi Zhuang

    WSDM 2019


    Deep Graph Infomax

    Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm

    ICLR 2019


    NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization

    Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie Tang

    WWW 2019


    Adversarial Training Methods for Network Embedding

    Quanyu Dai, Xiao Shen, Liang Zhang, Qiang Li, Dan Wang

    WWW 2019


    vGraph: A Generative Model for Joint Community Detection and Node Representation Learning

    Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang

    arXiv 1906


1.2   异构图中的节点表示学习

    Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networks

    Yann Jacob, Ludovic Denoyer, Patrick Gallinari

    WSDM 2014


    PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks

    Jian Tang, Meng Qu, Qiaozhu Mei

    KDD 2015


    Text Embedding, Heterogeneous Text Graphs

    Heterogeneous Network Embedding via Deep Architectures

    Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang

    KDD 2015


    Network Representation Learning with Rich Text Information

    Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Chang

    AAAI 2015


    Max-Margin DeepWalk: Discriminative Learning of Network Representation

    Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong Sun

    IJCAI 2016


    metapath2vec: Scalable Representation Learning for Heterogeneous Networks

    Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami

    KDD 2017


    Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks

    Jingbo Shang, Meng Qu, Jialu Liu, Lance M. Kaplan, Jiawei Han, Jian Peng

    arXiv 2016


    HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning

    Tao-yang Fu, Wang-Chien Lee, Zhen Lei

    CIKM 2017


    An Attention-based Collaboration Framework for Multi-View Network Representation Learning

    Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han

    CIKM 2017


 1.3   动态图中的节点表示学习

    Know-evolve: Deep temporal reasoning for dynamic knowledge graphs

    Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song

    ICML 2017


    Dyngem: Deep embedding method for dynamic graphs

    Palash Goyal, Nitin Kamra, Xinran He, Yan Liu

    ICLR 2017


     Workshop

    Attributed network embedding for learning in a dynamic environment

    Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu

    CIKM 2017


    Dynamic Network Embedding by Modeling Triadic Closure Process

    Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang

    AAAI 2018


    DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks

    Jianxin Ma, Peng Cui, Wenwu Zhu

    AAAI 2018


    TIMERS: Error-Bounded SVD Restart on Dynamic Networks

    Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, Wenwu Zhu

    AAAI 2018


    Dynamic Embeddings for User Profiling in Twitter

    Shangsong Liang, Xiangliang Zhang, Zhaochun Ren, Evangelos Kanoulas

    KDD 2018


    Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding

    Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang

    IJCAI 2018


    DyRep: Learning Representations over Dynamic Graphs

    Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha

    ICLR 2019


    Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks

    Srijan Kumar, Xikun Zhang, Jure Leskovec

    KDD2019


  2   知识图谱Embedding化

    Translating Embeddings for Modeling Multi-relational Data

    Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko

    NIPS 2013

    Knowledge Graph Embedding by Translating on Hyperplanes

    Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen

    AAAI 2014


    Learning Entity and Relation Embeddings for Knowledge Graph Completion

    Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu

    AAAI 2015


    Knowledge Graph Embedding via Dynamic Mapping Matrix

    Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, Jun Zha

    ACL 2015


    Modeling Relation Paths for Representation Learning of Knowledge Bases

    Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, Song Liu

    EMNLP 2015


    Embedding Entities and Relations for Learning and Inference in Knowledge Bases

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

    ICLR 2015


    Holographic Embeddings of Knowledge Graphs

    Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio

    AAAI 2016


    Complex Embeddings for Simple Link Prediction

    Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard

    ICML 2016


    Modeling Relational Data with Graph Convolutional Networks

    Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, Max Welling

    arXiv 2017

    .03

    Fast Linear Model for Knowledge Graph Embeddings

    Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomas Mikolov

    arXiv 2017

    .10

    Convolutional 2D Knowledge Graph Embeddings

    Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel

    AAAI 2018


    Knowledge Graph Embedding With Iterative Guidance From Soft Rules

    Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li Guo

    AAAI 2018


    KBGAN: Adversarial Learning for Knowledge Graph Embeddings

    Liwei Cai, William Yang Wang

    NAACL 2018


    Improving Knowledge Graph Embedding Using Simple Constraints

    Boyang Ding, Quan Wang, Bin Wang, Li Guo

    ACL 2018


    SimplE Embedding for Link Prediction in Knowledge Graphs

    Seyed Mehran Kazemi, David Poole

    NeurIPS 2018


    A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network

    Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung

    NAACL 2018


    Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

    Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei Zhang, Abraham Bernstein, Huajun Chen

    WWW 2019


    RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

    Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang

    ICLR 2019


    Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

    Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul

    ACL 2019


    Probabilistic Logic Neural Networks for Reasoning

    Meng Qu, Jian Tang

    arXiv 1906


    3   图神经网络

    Revisiting Semi-supervised Learning with Graph Embeddings

    Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov

    ICML 2016


    Semi-Supervised Classification with Graph Convolutional Networks

    Thomas N. Kipf, Max Welling

    ICLR 2017


    Neural Message Passing for Quantum Chemistry

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

    ICML 2017


    Motif-Aware Graph Embeddings

    Hoang Nguyen, Tsuyoshi Murata

    IJCAI 2017


    Learning Graph Representations with Embedding Propagation

    Alberto Garcia-Duran, Mathias Niepert

    NIPS 2017


    Inductive Representation Learning on Large Graphs

    William L. Hamilton, Rex Ying, Jure Leskovec

    NIPS 2017


    Graph Attention Networks

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

    ICLR 2018


    FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

    Jie Chen, Tengfei Ma, Cao Xiao

    ICLR 2018


    Representation Learning on Graphs with Jumping Knowledge Networks

    Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka

    ICML 2018


    Stochastic Training of Graph Convolutional Networks with Variance Reduction

    Jianfei Chen, Jun Zhu, Le Song

    ICML 2018


    Large-Scale Learnable Graph Convolutional Networks

    Hongyang Gao, Zhengyang Wang, Shuiwang Ji

    KDD 2018


    Adaptive Sampling Towards Fast Graph Representation Learning

    Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang

    NeurIPS 2018


    Hierarchical Graph Representation Learning with Differentiable Pooling

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

    NeurIPS 2018


    Bayesian Semi-supervised Learning with Graph Gaussian Processes

    Yin Cheng Ng, Nicolò Colombo, Ricardo Silva

    NeurIPS 2018


    Pitfalls of Graph Neural Network Evaluation

    Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann

    arXiv 2018

    .11

    Heterogeneous Graph Attention Network

    Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang Ye

    WWW 2019


    Bayesian graph convolutional neural networks for semi-supervised classification

    Yingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Üstebay

    AAAI 2019


    How Powerful are Graph Neural Networks?

    Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka

    ICLR 2019


    LanczosNet: Multi-Scale Deep Graph Convolutional Networks

    Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel

    ICLR 2019


    Graph Wavelet Neural Network

    Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng

    ICLR 2019


    Supervised Community Detection with Line Graph Neural Networks

    Zhengdao Chen, Xiang Li, Joan Bruna

    ICLR 2019


    Predict then Propagate: Graph Neural Networks meet Personalized PageRank

    Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann

    ICLR 2019


    Invariant and Equivariant Graph Networks

    Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman

    ICLR 2019


    Capsule Graph Neural Network

    Zhang Xinyi, Lihui Chen

    ICLR 2019


    MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing

    Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan

    ICML 2019


    Graph U-Nets

    Hongyang Gao, Shuiwang Ji

    ICML 2019


    Disentangled Graph Convolutional Networks

    Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu

    ICML 2019


    GMNN: Graph Markov Neural Networks

    Meng Qu, Yoshua Bengio, Jian Tang

    ICML 2019


    Simplifying Graph Convolutional Networks

    Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger

    ICML 2019


    Position-aware Graph Neural Networks

    Jiaxuan You, Rex Ying, Jure Leskovec

    ICML 2019


    Self-Attention Graph Pooling

    Junhyun Lee, Inyeop Lee, Jaewoo Kang

    ICML 2019


 4   图神经网络的应用

 4.1   自然语言处理

    Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

    Diego Marcheggiani, Ivan Titov

    EMNLP 2017


    Graph Convolutional Encoders for Syntax-aware Neural Machine Translation

    Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima’an

    EMNLP 2017


    Graph-based Neural Multi-Document Summarization

    Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, Dragomir Radev

    CoNLL 2017


    QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension

    Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le

    ICLR 2018


    A Structured Self-attentive Sentence Embedding

    Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, Yoshua Bengio

    ICLR 2018


    Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering

    Daniil Sorokin, Iryna Gurevych

    COLING 2018


    Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks

    Diego Marcheggiani, Joost Bastings, Ivan Titov

    NAACL 2018


    Linguistically-Informed Self-Attention for Semantic Role Labeling

    Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum

    EMNLP 2018


    Graph Convolution over Pruned Dependency Trees Improves Relation Extraction

    Yuhao Zhang, Peng Qi, Christopher D. Manning

    EMNLP 2018


    A Graph-to-Sequence Model for AMR-to-Text Generation

    Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea

    ACL 2018


    Graph-to-Sequence Learning using Gated Graph Neural Networks

    Daniel Beck, Gholamreza Haffari, Trevor Cohn

    ACL 2018


    Graph Convolutional Networks for Text Classification

    Liang Yao, Chengsheng Mao, Yuan Luo

    AAAI 2019


    Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder

    Caio Corro, Ivan Titov

    ICLR 2019

    .

    Structured Neural Summarization

    Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmid

    ICLR 2019


    Multi-task Learning over Graph Structures

    Pengfei Liu, Jie Fu, Yue Dong, Xipeng Qiu, Jackie Chi Kit Cheung

    AAAI 2019


    Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing

    Wenhan Xiong, Jiawei Wu, Deren Lei, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang

    NAACL 2019


    Single Document Summarization as Tree Induction

    Yang Liu, Ivan Titov, Mirella Lapata

    NAACL 2019


    Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks

    Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, Huajun Chen

    NAACL 2019


    Graph Neural Networks with Generated Parameters for Relation Extraction

    Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun

    ACL 2019


    Dynamically Fused Graph Network for Multi-hop Reasoning

    Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu

    ACL 2019


    Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media

    Chang Li, Dan Goldwasser

    ACL 2019


    Attention Guided Graph Convolutional Networks for Relation Extraction

    Zhijiang Guo, Yan Zhang, Wei Lu

    ACL 2019


    Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks

    Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar

    ACL 2019


    GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction

    Tsu-Jui Fu, Peng-Hsuan Li, Wei-Yun Ma

    ACL 2019


    Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs

    Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, Bowen Zhou

    ACL 2019


    Cognitive Graph for Multi-Hop Reading Comprehension at Scale

    Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang

    ACL 2019


    Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model

    Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, Xu Sun

    ACL 2019


    Matching Article Pairs with Graphical Decomposition and Convolutions

    Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu

    ACL 2019


    Embedding Imputation with Grounded Language Information

    Ziyi Yang, Chenguang Zhu, Vin Sachidananda, Eric Darve

    ACL 2019


    Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations

    Hongyang Gao, Yongjun Chen, Shuiwang Ji

    WWW 2019


   4.2   计算机视觉

    3D Graph Neural Networks for RGBD Semantic Segmentation

    Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun

    ICCV 2017


    Situation Recognition With Graph Neural Networks

    Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler

    ICCV 2017


    Graph-Based Classification of Omnidirectional Images

    Renata Khasanova, Pascal Frossard

    ICCV 2017


    Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

    Sijie Yan, Yuanjun Xiong, Dahua Lin

    AAAI 2018


    Image Generation from Scene Graphs

    Justin Johnson, Agrim Gupta, Li Fei-Fei

    CVPR 2018


    FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation

    Yaoqing Yang, Chen Feng, Yiru Shen, Dong Tian

    CVPR 2018


    PPFNet: Global Context Aware Local Features for Robust 3D Point Matching

    Haowen Deng, Tolga Birdal, Slobodan Ilic

    CVPR 2018


    Iterative Visual Reasoning Beyond Convolutions

    Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta

    CVPR 2018


    Surface Networks

    Ilya Kostrikov, Zhongshi Jiang, Daniele Panozzo, Denis Zorin, Joan Bruna

    CVPR 2018


    FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis

    Nitika Verma, Edmond Boyer, Jakob Verbeek

    CVPR 2018


    Learning to Act Properly: Predicting and Explaining Affordances From Images

    Ching-Yao Chuang, Jiaman Li, Antonio Torralba, Sanja Fidler

    CVPR 2018


    Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling

    Yiru Shen, Chen Feng, Yaoqing Yang, Dong Tian

    CVPR 2018


    Deformable Shape Completion With Graph Convolutional Autoencoders

    Or Litany, Alex Bronstein, Michael Bronstein, Ameesh Makadia

    CVPR 2018


    Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images

    Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, Yu-Gang Jiang

    ECCV 2018


    Learning Human-Object Interactions by Graph Parsing Neural Networks

    Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu

    ECCV 2018


    Generating 3D Faces using Convolutional Mesh Autoencoders

    Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, Michael J. Black

    ECCV 2018


    Learning SO(3) Equivariant Representations with Spherical CNNs

    Carlos Esteves, Christine Allen-Blanchette, Ameesh Makadia, Kostas Daniilidis

    ECCV 2018


    Neural Graph Matching Networks for Fewshot 3D Action Recognition

    Michelle Guo, Edward Chou, De-An Huang, Shuran Song, Serena Yeung, Li Fei-Fei

    ECCV 2018


    Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds

    Lasse Hansen, Jasper Diesel, Mattias P. Heinrich

    ECCV 2018


    Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network

    Feng Mao, Xiang Wu, Hui Xue, Rong Zhang

    ECCV 2018


    Graph R-CNN for Scene Graph Generation

    Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, Devi Parikh

    ECCV 2018


    Exploring Visual Relationship for Image Captioning

    Ting Yao, Yingwei Pan, Yehao Li, Tao Mei

    ECCV 2018


    Beyond Grids: Learning Graph Representations for Visual Recognition

    Yin Li, Abhinav Gupta

    NeurIPS 2018


    Learning Conditioned Graph Structures for Interpretable Visual Question Answering

    Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot

    NeurIPS 2018


    LinkNet: Relational Embedding for Scene Graph

    Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon

    NeurIPS 2018


    Flexible Neural Representation for Physics Prediction

    Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B. Tenenbaum, Daniel L. K. Yamins

    NeurIPS 2018


    Learning Localized Generative Models for 3D Point Clouds via Graph Convolution

    Diego Valsesia, Giulia Fracastoro, Enrico Magli

    ICLR 2019


    Graph-Based Global Reasoning Networks

    Yunpeng Chen, Marcus Rohrbach, Zhicheng Yan, Shuicheng Yan, Jiashi Feng, Yannis Kalantidis

    CVPR 2019


    Deep Graph Laplacian Regularization for Robust Denoising of Real Images

    Jin Zeng, Jiahao Pang, Wenxiu Sun, Gene Cheung

    CVPR 2019


    Learning Context Graph for Person Search

    Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang

    CVPR 2019


    Graphonomy: Universal Human Parsing via Graph Transfer Learning

    Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin

    CVPR 2019


    Masked Graph Attention Network for Person Re-Identification

    Liqiang Bao, Bingpeng Ma, Hong Chang, Xilin Chen

    CVPR 2019


    Learning to Cluster Faces on an Affinity Graph

    Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin

    CVPR 2019


    Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition

    Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, Qi Tian

    CVPR 2019


    Adaptively Connected Neural Networks

    Guangrun Wang, Keze Wang, Liang Lin

    CVPR 2019


    MeshCNN: A Network with an Edge

    Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or

    SIGGRAPH 2019

https://ranahanocka.github.io/MeshCNN/


4.3   推荐系统

    Graph Convolutional Neural Networks for Web-Scale Recommender Systems

    Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec

    KDD 2018


    PinSage

    SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation

    Le Wu, Peijie Sun, Richang Hong, Yanjie Fu, Xiting Wang, Meng Wang

    AAAI 2018


    GCN, Social recommendation

    Session-based Social Recommendation via Dynamic Graph Attention Networks

    Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang

    WSDM 2019


    Social recommendation, session-based, GAT

    Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

    Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen

    WWW 2019


    Social recommendation, GAT

    Graph Neural Networks for Social Recommendation

    Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin

    WWW 2019


    Social recommendation, GNN

    Session-based Recommendation with Graph Neural Networks

    Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan

    AAAI 2019


    Session-based recommendation, GNN

    A Neural Influence Diffusion Model for Social Recommendation

    Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang

    SIGIR 2019


    Social Recommendation, diffusion

    Neural Graph Collaborative Filtering

    Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua

    SIGIR 2019


    Collaborative Filtering, GNN

    Binarized Collaborative Filtering with Distilling Graph Convolutional Networks

    Haoyu Wang, Defu Lian, Yong Ge

    IJCAI 2019


    4.4   链接预测

    Link Prediction Based on Graph Neural Networks

    Muhan Zhang, Yixin Chen

    NeurIPS 2018


    Link Prediction via Subgraph Embedding-Based Convex Matrix Completion

    Zhu Cao, Linlin Wang, Gerard de Melo

    AAAI 2018


    Graph Convolutional Matrix Completion

    Rianne van den Berg, Thomas N. Kipf, Max Welling

    KDD 2018

     Workshop


4.5   影响力预测

    DeepInf: Social Influence Prediction with Deep Learning

    Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang

    KDD 2018


    Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks

    Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos

    KDD 2019


 4.6   神经架构搜索

    Graph HyperNetworks for Neural Architecture Search

    Chris Zhang, Mengye Ren, Raquel Urtasun

    ICLR 2019


 4.7   强化学习

    Action Schema Networks: Generalised Policies with Deep Learning

    Sam Toyer, Felipe Trevizan, Sylvie Thiebaux, Lexing Xie

    AAAI 2018


    NerveNet: Learning Structured Policy with Graph Neural Networks

    Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler

    ICLR 2018


    Graph Networks as Learnable Physics Engines for Inference and Control

    Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller

    ICML 2018


    Learning Policy Representations in Multiagent Systems

    Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards

    ICML 2018


    Relational recurrent neural networks

    Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski,Théophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy Lillicrap

    NeurIPS 2018


    Transfer of Deep Reactive Policies for MDP Planning

    Aniket Bajpai, Sankalp Garg, Mausam

    NeurIPS 2018


    Neural Graph Evolution: Towards Efficient Automatic Robot Design

    Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba

    ICLR 2019


   4.8   组合优化

    Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search

    Zhuwen Li, Qifeng Chen, Vladlen Koltun

    NeurIPS 2018


    Reinforcement Learning for Solving the Vehicle Routing Problem

    Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takáč

    NeurIPS 2018


 4.9  对抗性攻击

    Adversarial Attack on Graph Structured Data

    Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song

    ICML 2018


    Adversarial Attacks on Neural Networks for Graph Data

    Daniel Zügner, Amir Akbarnejad, Stephan Günnemann

    KDD 2018


    Adversarial Attacks on Graph Neural Networks via Meta Learning

    Daniel Zügner, Stephan Günnemann

    ICLR 2019


 4.10   元学习

    Learning Steady-States of Iterative Algorithms over Graphs

    Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song

    ICML 2018


 4.11   结构学习

    Few-Shot Learning with Graph Neural Networks

    Victor Garcia, Joan Bruna

    ICLR 2018


    Neural Relational Inference for Interacting Systems

    Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel

    ICML 2018


    Brain Signal Classification via Learning Connectivity Structure

    Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee

    arXiv 1905


    A Flexible Generative Framework for Graph-based Semi-supervised Learning

    Jiaqi Ma, Weijing Tang, Ji Zhu, Qiaozhu Mei

    arXiv 1905


    Joint embedding of structure and features via graph convolutional networks

    Sébastien Lerique, Jacob Levy Abitbol, Márton Karsai

    arXiv 1905


    Variational Spectral Graph Convolutional Networks

    Louis Tiao, Pantelis Elinas, Harrison Nguyen, Edwin V. Bonilla

    arXiv 1906


    Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning

    Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang

    ICLR 2019


    Graph Learning Network: A Structure Learning Algorithm

    Darwin Saire Pilco, Adín Ramírez Rivera

    ICML 2019

     Workshop

    Learning Discrete Structures for Graph Neural Networks

    Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He

    ICML 2019


    Graphite: Iterative Generative Modeling of Graphs

    Aditya Grover, Aaron Zweig, Stefano Ermon

    ICML 2019


    4.12   生物信息学和化学

    Protein Interface Prediction using Graph Convolutional Networks

    Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur

    NeurIPS 2017


    Modeling Polypharmacy Side Effects with Graph Convolutional Networks

    Marinka Zitnik, Monica Agrawal, Jure Leskovec

    Bioinformatics 2018


    NeoDTI: Neural Integration of Neighbor Information from a Heterogeneous Network for Discovering New Drug–target Interactions

    Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang Zeng

    Bioinformatics 2018


    SELFIES: a Robust Representation of Semantically Constrained Graphs with an Example Application in Chemistry

    Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik

    arXiv 1905


    Drug-Drug Adverse Effect Prediction with Graph Co-Attention

    Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, Jian Tang

    arXiv 1905


 4.13   定理证明

    Premise Selection for Theorem Proving by Deep Graph Embedding

    Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng

    NeurIPS 2017


    5   图生成

    GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

    Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec

    ICML 2018


    NetGAN: Generating Graphs via Random Walks

    Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann

    ICML 2018


    Junction Tree Variational Autoencoder for Molecular Graph Generation

    Wengong Jin, Regina Barzilay, Tommi Jaakkola

    ICML 2018


    MolGAN: An implicit generative model for small molecular graphs

    Nicola De Cao, Thomas Kipf

    arXiv 1805

    Generative Modeling for Protein Structures

    Namrata Anand, Po-Ssu Huang

    NeurIPS 2018


    Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders

    Tengfei Ma, Jie Chen, Cao Xiao

    NeurIPS 2018


    Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation

    Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec

    NeurIPS 2018


    Constrained Graph Variational Autoencoders for Molecule Design

    Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt

    NeurIPS 2018


    Learning Multimodal Graph-to-Graph Translation for Molecule Optimization

    Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola

    ICLR 2019


    DAG-GNN: DAG Structure Learning with Graph Neural Networks

    Yue Yu, Jie Chen, Tian Gao, Mo Yu

    ICML 2019


    Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation

    Mingming Sun, Ping Li

    AISTATS 2019


 6   图形布局和高维数据可视化

    Visualizing Data using t-SNE

    Laurens van der Maaten, Geoffrey Hinton

    JMLR 2008

    Visualizing non-metric similarities in multiple maps

    Laurens van der Maaten, Geoffrey Hinton

    ML 2012


    Visualizing Large-scale and High-dimensional Data

    Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei

    WWW 2016


    GraphTSNE: A Visualization Technique for Graph-Structured Data

    Yao Yang Leow, Thomas Laurent, Xavier Bresson

    ICLR 2019

     Workshop


    7   图表示学习系统

    GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

    Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang

    WWW 2019


    PyTorch-BigGraph: A Large-scale Graph Embedding System

    Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, Alex Peysakhovich

    SysML 2019


    AliGraph: A Comprehensive Graph Neural Network Platform

    Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou

    VLDB 2019


    Deep Graph Library

    DGL Team

    AmpliGraph

    Luca Costabello, Sumit Pai, Chan Le Van, Rory McGrath, Nicholas McCarthy, Pedro Tabacof

    Euler

    Alimama Engineering Platform Team, Alimama Search Advertising Algorithm Team

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Jian Tang博士是DiDi AI Labs的智能控制首席科学家。 他还是锡拉丘兹大学的教授。 他是IEEE的研究员。 他的研究兴趣在于机器学习,大数据,云计算和无线网络领域。 他已经在顶级期刊和会议上发表了130多篇论文。 他于2009年获得了NSF CAREER奖。他曾担任过几本IEEE期刊的编辑。 此外,他还担任过2018年移动和普适系统国际会议:计算的TPC联合主席。 担任2019 IEEE国际计算机通信会议(INFOCOM)的TPC副主席; 作为INFOCOM 2017-2018的区域TPC主席。 他目前是IEEE Communications Society的通信交换和路由委员会的副主席。
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