Literature of Deep Learning for Graphs

This is a paper list about deep learning for graphs.

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1 2008
- 1.1 JMLR
2 2011
- 2.1 ICML
3 2012
- 3.1 ML
4 2013
- 4.1 NIPS
5 2014
- 5.1 WSDM
- 5.2 AAAI
- 5.3 KDD
- 5.4 NIPS
6 2015
- 6.1 AAAI
- 6.2 ACL
- 6.3 CIKM
- 6.4 EMNLP
- 6.5 ICLR
- 6.6 IEEE
- 6.7 KDD
- 6.8 WWW
7 2016
- 7.1 arXiv
- 7.2 IJCAI
- 7.3 AAAI
- 7.4 ICML
- 7.5 WWW
- 7.6 KDD
8 2017
- 8.1 AAAI
- 8.2 ACL
- 8.3 CIKM
- 8.4 CoNLL
- 8.5 EMNLP
- 8.6 ICCV
- 8.7 ICLR
- 8.8 ICML
- 8.9 IJCAI
- 8.10 KDD
- 8.11 NIPS
- 8.12 NeurIPS
- 8.13 arXiv
9 2018
- 9.1 AAAI
- 9.2 ACL
- 9.3 Bioinformatics
- 9.4 CIKM
- 9.5 COLING
- 9.6 CVPR
- 9.7 ECCV
- 9.8 EMNLP
- 9.9 ICDM
- 9.10 ICLR
- 9.11 ICML
- 9.12 IJCAI
- 9.13 KDD
- 9.14 NAACL
- 9.15 NeurIPS
- 9.16 SIGIR
- 9.17 WSDM
- 9.18 WWW
- 9.19 arXiv
10 2019
- 10.1 WSDM
- 10.2 ICLR
- 10.3 WWW
- 10.4 AAAI
- 10.5 ICML
- 10.6 arXiv
- 10.7 NAACL
- 10.8 CVPR
- 10.9 SIGGRAPH
- 10.10 SIGIR
- 10.11 IJCAI
- 10.12 bioRxiv
- 10.13 ICSC
- 10.14 Nature
- 10.15 AISTATS
- 10.16 SysML
- 10.17 VLDB
- 10.18 ACL
- 10.19 KDD
- 10.20 ICCV
- 10.21 EMNLP
- 10.22 NeurIPS
11 2020
- 11.1 ICLR
12 Others

1 2008

1.1 JMLR

Visualizing Data Using T-sne

Laurens, van, der, Maaten, Geoffrey, Hinton

JMLR 2008

2 2011

2.1 ICML

A Three-way Model for Collective Learning on Multi-relational Data.

Maximilian, Nickel, Volker, Tresp, Hans-Peter, Kriegel

ICML 2011

3 2012

3.1 ML

Visualizing Non-metric Similarities in Multiple Maps

Laurens, van, der, Maaten, Geoffrey, Hinton

ML 2012

4 2013

4.1 NIPS

Translating Embeddings for Modeling Multi-relational Data

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

NIPS 2013

5 2014

5.1 WSDM

Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networks

Yann, Jacob, Ludovic, Denoyer, Patrick, Gallinari

WSDM 2014

5.2 AAAI

Knowledge Graph Embedding by Translating on Hyperplanes

Zhen, Wang, Jianwen, Zhang, Jianlin, Feng, Zheng, Chen

AAAI 2014

5.3 KDD

Deepwalk: Online Learning of Social Representations

Bryan, Perozzi, Rami, Al-Rfou, Steven, Skiena

KDD 2014

Node, classification, Random, walk, Skip-gram

5.4 NIPS

Reducing the Rank of Relational Factorization Models by Including Observable Patterns

Maximilian, Nickel, Xueyan, Jiang, Volker, Tresp

NIPS 2014

6 2015

6.1 AAAI

Network Representation Learning with Rich Text Information

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

AAAI 2015

Learning Entity and Relation Embeddings for Knowledge Graph Completion

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

AAAI 2015

6.2 ACL

Knowledge Graph Embedding via Dynamic Mapping Matrix

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

ACL 2015

6.3 CIKM

Grarep: Learning Graph Representations with Global Structural Information

Shaosheng, Cao, Wei, Lu, Qiongkai, Xu

CIKM 2015

High-order, SVD

6.4 EMNLP

Modeling Relation Paths for Representation Learning of Knowledge Bases

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

EMNLP 2015

6.5 ICLR

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

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

ICLR 2015

6.6 IEEE

A Review of Relational Machine Learning for Knowledge Graph

Maximilian, Nickel, Kevin, Murphy, Volker, Tresp, Evgeniy, Gabrilovich

IEEE 2015

6.7 KDD

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

6.8 WWW

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

7 2016

7.1 arXiv

Variational Graph Auto-encoders

Thomas, N., Kipf, Max, Welling

arXiv 2016

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

7.2 IJCAI

Max-margin Deepwalk: Discriminative Learning of Network Representation

Cunchao, Tu, Weicheng, Zhang, Zhiyuan, Liu, Maosong, Sun

IJCAI 2016

7.3 AAAI

Holographic Embeddings of Knowledge Graphs

Maximilian, Nickel, Lorenzo, Rosasco, Tomaso, Poggio

AAAI 2016

7.4 ICML

Complex Embeddings for Simple Link Prediction

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

ICML 2016

Revisiting Semi-supervised Learning with Graph Embeddings

Zhilin, Yang, William, W., Cohen, Ruslan, Salakhutdinov

ICML 2016

7.5 WWW

Visualizing Large-scale and High-dimensional Data

Jian, Tang, Jingzhou, Liu, Ming, Zhang, Qiaozhu, Mei

WWW 2016

7.6 KDD

Node2vec: Scalable Feature Learning for Networks

Aditya, Grover, Jure, Leskovec

KDD 2016

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

8 2017

8.1 AAAI

Scalable Graph Embedding for Asymmetric Proximity

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

AAAI 2017

8.2 ACL

Cane: Context-aware Network Embedding for Relation Modeling

Cunchao, Tu, Han, Liu, Zhiyuan, Liu, Maosong, Sun

ACL 2017

8.3 CIKM

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

Multi-view Clustering with Graph Embedding for Connectome Analysis

Guixiang, Ma, Lifang, He, Chun-Ta, Lu, Weixiang, Shao, Philip, S., Yu, Alex, D., Leow, Ann, B., Ragin

CIKM 2017

Attributed Signed Network Embedding

Suhang, Wang, Charu, Aggarwal, Jiliang, Tang, Huan, Liu

CIKM 2017

Attributed Network Embedding for Learning in a Dynamic Environment

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

CIKM 2017

8.4 CoNLL

Graph-based Neural Multi-document Summarization

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

CoNLL 2017

8.5 EMNLP

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

8.6 ICCV

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

8.7 ICLR

Dyngem: Deep Embedding Method for Dynamic Graphs

Palash, Goyal, Nitin, Kamra, Xinran, He, Yan, Liu

ICLR 2017 Workshop

Semi-supervised Classification with Graph Convolutional Networks

Thomas, N., Kipf, Max, Welling

ICLR 2017

8.8 ICML

Know-evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs

Rakshit, Trivedi, Hanjun, Dai, Yichen, Wang, Le, Song

ICML 2017

Neural Message Passing for Quantum Chemistry

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

ICML 2017

8.9 IJCAI

Fast Network Embedding Enhancement via High Order Proximity Approximation

Cheng, Yang, Maosong, Sun, Zhiyuan, Liu, Cunchao, Tu

IJCAI 2017

Motif-aware Graph Embeddings

Hoang, Nguyen, Tsuyoshi, Murata

IJCAI 2017

8.10 KDD

Struc2vec: Learning Node Representations from Structural Identity

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

KDD 2017

Structural, Identity

Metapath2vec: Scalable Representation Learning for Heterogeneous Networks

Yuxiao, Dong, Nitesh, V., Chawla, Ananthram, Swami

KDD 2017

8.11 NIPS

Poincaré Embeddings for Learning Hierarchical Representations

Maximilian, Nickel, Douwe, Kiela

NIPS 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

8.12 NeurIPS

Learning Combinatorial Optimization Algorithms over Graphs

Hanjun, Dai, Elias, B., Khalil, Yuyu, Zhang, Bistra, Dilkina, Le, Song

NeurIPS 2017

Protein Interface Prediction Using Graph Convolutional Networks

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

NeurIPS 2017

Premise Selection for Theorem Proving by Deep Graph Embedding

Mingzhe, Wang, Yihe, Tang, Jian, Wang, Jia, Deng

NeurIPS 2017

8.13 arXiv

Modeling Relational Data with Graph Convolutional Networks

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

arXiv 2017

Fast Linear Model for Knowledge Graph Embeddings

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

arXiv 2017

9 2018

9.1 AAAI

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

Starspace: Embed All The Things

Ledell, Wu, Adam, Fisch, Sumit, Chopra, Keith, Adams, Antoine, Bordes, Jason, Weston

AAAI 2018

Generative Adversarial Network Based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation

Xiaoyan, Cai, Junwei, Han, Libin, Yang

AAAI 2018

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

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

Spatial Temporal Graph Convolutional Networks for Skeleton-based Action Recognition

Sijie, Yan, Yuanjun, Xiong, Dahua, Lin

AAAI 2018

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

Link Prediction via Subgraph Embedding-based Convex Matrix Completion

Zhu, Cao, Linlin, Wang, Gerard, de, Melo

AAAI 2018

Action Schema Networks: Generalised Policies with Deep Learning

Sam, Toyer, Felipe, Trevizan, Sylvie, Thiebaux, Lexing, Xie

AAAI 2018

9.2 ACL

Improving Knowledge Graph Embedding Using Simple Constraints

Boyang, Ding, Quan, Wang, Bin, Wang, Li, Guo

ACL 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

9.3 Bioinformatics

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

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

Bioinformatics 2018

9.4 CIKM

Regal: Representation Learning-based Graph Alignment

Mark, Heimann, Haoming, Shen, Tara, Safavi, Danai, Koutra

CIKM 2018

9.5 COLING

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

Daniil, Sorokin, Iryna, Gurevych

COLING 2018

9.6 CVPR

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

9.7 ECCV

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

9.8 EMNLP

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

Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks

Zhichun, Wang, Qingsong, Lv, Xiaohan, Lan, Yu, Zhang

EMNLP 2018

9.9 ICDM

Meta-graph Based Hin Spectral Embedding: Methods, Analyses, and Insights

Carl, Yang, Yichen, Feng, Pan, Li, Yu, Shi, Jiawei, Han

ICDM 2018

9.10 ICLR

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

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

Nervenet: Learning Structured Policy with Graph Neural Networks

Tingwu, Wang, Renjie, Liao, Jimmy, Ba, Sanja, Fidler

ICLR 2018

Few-shot Learning with Graph Neural Networks

Victor, Garcia, Joan, Bruna

ICLR 2018

9.11 ICML

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

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

Adversarial Attack on Graph Structured Data

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

ICML 2018

Learning Steady-states of Iterative Algorithms over Graphs

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

ICML 2018

Neural Relational Inference for Interacting Systems

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

ICML 2018

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

Learning Deep Generative Models of Graphs

Yujia, Li, Oriol, Vinyals, Chris, Dyer, Razvan, Pascanu, Peter, Battaglia

ICML 2018

Junction Tree Variational Autoencoder for Molecular Graph Generation

Wengong, Jin, Regina, Barzilay, Tommi, Jaakkola

ICML 2018

9.12 IJCAI

Anrl: Attributed Network Representation Learning via Deep Neural Networks

Zhen, Zhang, Hongxia, Yang, Jiajun, Bu, Sheng, Zhou, Pinggang, Yu, Jianwei, Zhang, Martin, Ester, Can, Wang

IJCAI 2018

Efficient Attributed Network Embedding via Recursive Randomized Hashing

Wei, Wu, Bin, Li, Ling, Chen, Chengqi, Zhang

IJCAI 2018

Deep Attributed Network Embedding

Hongchang, Gao, Heng, Huang

IJCAI 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

9.13 KDD

Learning Structural Node Embeddings via Diffusion Wavelets

Claire, Donnat, Marinka, Zitnik, David, Hallac, Jure, Leskovec

KDD 2018

Pme: Projected Metric Embedding on Heterogeneous Networks for Link Prediction

Hongxu, Chen, Hongzhi, Yin, Weiqing, Wang, Hao, Wang, Quoc, Viet, Hung, Nguyen, Xue, Li

KDD 2018

Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks

Yu, Shi, Qi, Zhu, Fang, Guo, Chao, Zhang, Jiawei, Han

KDD 2018

Dynamic Embeddings for User Profiling in Twitter

Shangsong, Liang, Xiangliang, Zhang, Zhaochun, Ren, Evangelos, Kanoulas

KDD 2018

Large-scale Learnable Graph Convolutional Networks

Hongyang, Gao, Zhengyang, Wang, Shuiwang, Ji

KDD 2018

Graph Convolutional Neural Networks for Web-scale Recommender Systems

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

KDD 2018

P, i, n, S, a, g, e

Graph Convolutional Matrix Completion

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

KDD 2018 Workshop

Deepinf: Social Influence Prediction with Deep Learning

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

KDD 2018

Adversarial Attacks on Neural Networks for Graph Data

Daniel, Zügner, Amir, Akbarnejad, Stephan, Günnemann

KDD 2018

9.14 NAACL

Kbgan: Adversarial Learning for Knowledge Graph Embeddings

Liwei, Cai, William, Yang, Wang

NAACL 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

Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks

Diego, Marcheggiani, Joost, Bastings, Ivan, Titov

NAACL 2018

9.15 NeurIPS

Simple Embedding for Link Prediction in Knowledge Graphs

Seyed, Mehran, Kazemi, David, Poole

NeurIPS 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

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

Link Prediction Based on Graph Neural Networks

Muhan, Zhang, Yixin, Chen

NeurIPS 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

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

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

9.16 SIGIR

Bine: Bipartite Network Embedding

Ming, Gao, Leihui, Chen, Xiangnan, He, Aoying, Zhou

SIGIR 2018

9.17 WSDM

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

Exploring Expert Cognition for Attributed Network Embedding

Xiao, Huang, Qingquan, Song, Jundong, Li, Xia, Hu

WSDM 2018

Shine: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction

Hongwei, Wang, Fuzheng, Zhang, Min, Hou, Xing, Xie, Minyi, Guo, Qi, Liu

WSDM 2018

Multidimensional Network Embedding with Hierarchical Structures

Yao, Ma, Zhaochun, Ren, Ziheng, Jiang, Jiliang, Tang, Dawei, Yin

WSDM 2018

Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning

Meng, Qu, Jian, Tang, Jiawei, Han

WSDM 2018

9.18 WWW

Verse: Versatile Graph Embeddings from Similarity Measures

Anton, Tsitsulin, Davide, Mottin, Panagiotis, Karras, Emmanuel, Müller

WWW 2018

Co-regularized Deep Multi-network Embedding

Jingchao, Ni, Shiyu, Chang, Xiao, Liu, Wei, Cheng, Haifeng, Chen, Dongkuan, Xu, Xiang, Zhang

WWW 2018

Side: Representation Learning in Signed Directed Networks

Junghwan, Kim, Haekyu, Park, Ji-Eun, Lee, U, Kang

WWW 2018

9.19 arXiv

Pitfalls of Graph Neural Network Evaluation

Oleksandr, Shchur, Maximilian, Mumme, Aleksandar, Bojchevski, Stephan, Günnemann

arXiv 2018

Molgan: An Implicit Generative Model for Small Molecular Graphs

Nicola, De, Cao, Thomas, Kipf

arXiv 2018

10 2019

10.1 WSDM

A General View for Network Embedding As Matrix Factorization

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

WSDM 2019

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

10.2 ICLR

Deep Graph Infomax

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

ICLR 2019

Dyrep: Learning Representations over Dynamic Graphs

Rakshit, Trivedi, Mehrdad, Farajtabar, Prasenjeet, Biswal, Hongyuan, Zha

ICLR 2019

Rotate: Knowledge Graph Embedding by Relational Rotation in Complex Space

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

ICLR 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

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

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

Diego, Valsesia, Giulia, Fracastoro, Enrico, Magli

ICLR 2019

Graph Hypernetworks for Neural Architecture Search

Chris, Zhang, Mengye, Ren, Raquel, Urtasun

ICLR 2019

Neural Graph Evolution: Towards Efficient Automatic Robot Design

Tingwu, Wang, Yuhao, Zhou, Sanja, Fidler, Jimmy, Ba

ICLR 2019

Attention, Learn to Solve Routing Problems!

Wouter, Kool, Herke, van, Hoof, Max, Welling

ICLR 2019

Learning a Sat Solver from Single-bit Supervision

Daniel, Selsam, Matthew, Lamm, Benedikt, Bünz, Percy, Liang, Leonardo, de, Moura, David, L., Dill

ICLR 2019

Adversarial Attacks on Graph Neural Networks via Meta Learning

Daniel, Zügner, Stephan, Günnemann

ICLR 2019

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

Learning Multimodal Graph-to-graph Translation for Molecule Optimization

Wengong, Jin, Kevin, Yang, Regina, Barzilay, Tommi, Jaakkola

ICLR 2019

Generative Code Modeling with Graphs

Marc, Brockschmidt, Miltiadis, Allamanis, Alexander, L., Gaunt, Oleksandr, Polozov

ICLR 2019

Graphtsne: A Visualization Technique for Graph-structured Data

Yao, Yang, Leow, Thomas, Laurent, Xavier, Bresson

ICLR 2019 Workshop

10.3 WWW

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

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

Heterogeneous Graph Attention Network

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

WWW 2019

Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations

Hongyang, Gao, Yongjun, Chen, Shuiwang, Ji

WWW 2019

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

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

Graphvite: A High-performance Cpu-gpu Hybrid System for Node Embedding

Zhaocheng, Zhu, Shizhen, Xu, Meng, Qu, Jian, Tang

WWW 2019

10.4 AAAI

Bayesian Graph Convolutional Neural Networks for Semi-supervised Classification

Yingxue, Zhang, Soumyasundar, Pal, Mark, Coates, Deniz, Üstebay

AAAI 2019

Graph Convolutional Networks for Text Classification

Liang, Yao, Chengsheng, Mao, Yuan, Luo

AAAI 2019

Multi-task Learning over Graph Structures

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

AAAI 2019

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

Atomic: an Atlas of Machine Commonsense for If-then Reasoning

Maarten, Sap, Ronan, Le, Bras, Emily, Allaway, Chandra, Bhagavatula, Nicholas, Lourie, Hannah, Rashkin, Brendan, Roof, Noah, A., Smith, Yejin, Choi

AAAI 2019

10.5 ICML

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

Relational Pooling for Graph Representations

Ryan, L., Murphy, Balasubramaniam, Srinivasan, Vinayak, Rao, Bruno, Ribeiro

ICML 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

Drug-drug Adverse Effect Prediction with Graph Co-attention

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

ICML 2019 Workshop

Dag-gnn: Dag Structure Learning with Graph Neural Networks

Yue, Yu, Jie, Chen, Tian, Gao, Mo, Yu

ICML 2019

10.6 arXiv

Continuous Graph Neural Networks

Louis-Pascal, A., C., Xhonneux, Meng, Qu, Jian, Tang

arXiv 2019

An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem

Chaitanya, K., Joshi, Thomas, Laurent, Xavier, Bresson

arXiv 2019

Brain Signal Classification via Learning Connectivity Structure

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

arXiv 2019

Joint Embedding of Structure and Features via Graph Convolutional Networks

Sébastien, Lerique, Jacob, Levy, Abitbol, Márton, Karsai

arXiv 2019

Variational Spectral Graph Convolutional Networks

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

arXiv 2019

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 2019

Detecting Drug-drug Interactions Using Artificial Neural Networks and Classic Graph Similarity Measures

Guy, Shtar, Lior, Rokach, Bracha, Shapira

arXiv 2019

10.7 NAACL

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

10.8 CVPR

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

Reasoning Visual Dialogs with Structural and Partial Observations

Zilong, Zheng, Wenguan, Wang, Siyuan, Qi, Song-Chun, Zhu

CVPR 2019

10.9 SIGGRAPH

Meshcnn: A Network with an Edge

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

SIGGRAPH 2019

h, t, t, p, s, :, /, /, r, a, n, a, h, a, n, o, c, k, a, ., g, i, t, h, u, b, ., i, o, /, M, e, s, h, C, N, N, /

10.10 SIGIR

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

10.11 IJCAI

Binarized Collaborative Filtering with Distilling Graph Convolutional Networks

Haoyu, Wang, Defu, Lian, Yong, Ge

IJCAI 2019

10.12 bioRxiv

Pgcn: Disease Gene Prioritization by Disease and Gene Embedding through Graph Convolutional Neural Networks

Yu, Li, Hiroyuki, Kuwahara, Peng, Yang, Le, Song, Xin, Gao

bioRxiv 2019

10.13 ICSC

Identifying Protein-protein Interaction Using Tree Lstm and Structured Attention

Mahtab, Ahmed, Jumayel, Islam, Muhammad, Rifayat, Samee, Robert, E., Mercer

ICSC 2019

10.14 Nature

Towards Perturbation Prediction of Biological Networks Using Deep Learning

Diya, Li, Jianxi, Gao

Nature 2019

10.15 AISTATS

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

Mingming, Sun, Ping, Li

AISTATS 2019

10.16 SysML

Pytorch-biggraph: A Large-scale Graph Embedding System

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

SysML 2019

10.17 VLDB

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

10.18 ACL

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

Deepak, Nathani, Jatin, Chauhan, Charu, Sharma, Manohar, Kaul

ACL 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

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

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

Chang, Li, Dan, Goldwasser

ACL 2019

A Neural Multi-digraph Model for Chinese Ner with Gazetteers

Ruixue, Ding, Pengjun, Xie, Xiaoyan, Zhang, Wei, Lu, Linlin, Li, Luo, Si

ACL 2019

Tree Communication Models for Sentiment Analysis

Yuan, Zhang, Yue, Zhang

ACL 2019

A2n: Attending to Neighbors for Knowledge Graph Inference

Trapit, Bansal, Da-Cheng, Juan, Sujith, Ravi, Andrew, McCallum

ACL 2019

Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension

Daesik, Kim, Seonhoon, Kim, Nojun, Kwak

ACL 2019

Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution

Yinchuan, Xu, Junlin, Yang

ACL 2019 Workshop

h, t, t, p, s, :, /, /, g, i, t, h, u, b, ., c, o, m, /, i, a, n, y, c, x, u, /, R, G, C, N, -, w, i, t, h, -, B, E, R, T

10.19 KDD

Progan: Network Embedding via Proximity Generative Adversarial Network

Hongchang, Gao, Jian, Pei, Heng, Huang

KDD 2019

Learning Network-to-network Model for Content-rich Network Embedding

:authors:` Zhicheng, He, Jie, Liu, Na, Li, Yalou, Huang`

KDD 2019

Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks

Srijan, Kumar, Xikun, Zhang, Jure, Leskovec

KDD 2019

Graph Representation Learning via Hard and Channel-wise Attention Networks

Hongyang, Gao, Shuiwang, Ji

KDD 2019

Conditional Random Field Enhanced Graph Convolutional Neural Networks

Hongchang, Gao, Jian, Pei, Heng, Huang

KDD 2019

Cluster-gcn: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

Wei-Lin, Chiang, Xuanqing, Liu, Si, Si, Yang, Li, Samy, Bengio, Cho-Jui, Hsieh

KDD 2019

Demo-net: Degree-specific Graph Neural Networks for Node and Graph Classification

Jun, Wu, Jingrui, He, Jiejun, Xu

KDD 2019

Hetgnn: Heterogeneous Graph Neural Network

Chuxu, Zhang, Dongjin, Song, Chao, Huang, Ananthram, Swami, Nitesh, V., Chawla

KDD 2019

Graph Recurrent Networks with Attributed Random Walks

Xiao, Huang, Qingquan, Song, Yuening, Li, Xia, Hu

KDD 2019

Graph Convolutional Networks with Eigenpooling

Yao, Ma, Suhang, Wang, Charu, Aggarwal, Jiliang, Tang

KDD 2019

Intentgc: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation

Jun, Zhao, Zhou, Zhou, Ziyu, Guan, Wei, Zhao, Wei, Ning, Guang, Qiu, Xiaofei, He

KDD 2019

An End-to-end Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation

Yanru, Qu, Ting, Bai, Weinan, Zhang, Jianyun, Nie, Jian, Tang

KDD 2019 Workshop

Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks

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

KDD 2019

Robust Graph Convolutional Networks Against Adversarial Attacks

Dingyuan, Zhu, Ziwei, Zhang, Peng, Cui, Wenwu, Zhu

KDD 2019

Certifiable Robustness and Robust Training for Graph Convolutional Networks

Daniel, Zügner, Stephan, Günnemann

KDD 2019

Gcn-mf: Disease-gene Association Identification By Graph Convolutional Networks and Matrix Factorization

Peng, Han, Peng, Yang, Peilin, Zhao, Shuo, Shang, Yong, Liu, Jiayu, Zhou, Xin, Gao, Panos, Kalnis

KDD 2019

Gcn-mf: Disease-gene Association Identification By Graph Convolutional Networks and Matrix Factorization

Peng, Han, Peng, Yang, Peilin, Zhao, Shuo, Shang, Yong, Liu, Jiayu, Zhou, Xin, Gao, Panos, Kalnis

KDD 2019

10.20 ICCV

Deepgcns: Can Gcns Go As Deep As Cnns?

Guohao, Li, Matthias, Muller, Ali, Thabet, Bernard, Ghanem

ICCV 2019

Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning

Jiwoong, Park, Minsik, Lee, Hyung, Jin, Chang, Kyuewang, Lee, Jin, Young, Choi

ICCV 2019

Pixel2mesh++: Multi-view 3d Mesh Generation via Deformation

Chao, Wen, Yinda, Zhang, Zhuwen, Li, Yanwei, Fu

ICCV 2019

Learning Trajectory Dependencies for Human Motion Prediction

Wei, Mao, Miaomiao, Liu, Mathieu, Salzmann, Hongdong, Li

ICCV 2019

Graph-based Object Classification for Neuromorphic Vision Sensing

Yin, Bi, Aaron, Chadha, Alhabib, Abbas, Eirina, Bourtsoulatze, Yiannis, Andreopoulos

ICCV 2019

Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid

Zhanghui, Kuang, Yiming, Gao, Guanbin, Li, Ping, Luo, Yimin, Chen, Liang, Lin, Wayne, Zhang

ICCV 2019

Understanding Human Gaze Communication by Spatio-temporal Graph Reasoning

Lifeng, Fan, Wenguan, Wang, Siyuan, Huang, Xinyu, Tang, Song-Chun, Zhu

ICCV 2019

Visual Semantic Reasoning for Image-text Matching

Kunpeng, Li, Yulun, Zhang, Kai, Li, Yuanyuan, Li, Yun, Fu

ICCV 2019

Graph Convolutional Networks for Temporal Action Localization

Runhao, Zeng, Wenbing, Huang, Mingkui, Tan, Yu, Rong, Peilin, Zhao, Junzhou, Huang, Chuang, Gan

ICCV 2019

Learning Combinatorial Embedding Networks for Deep Graph Matching

Runzhong, Wang, Junchi, Yan, Xiaokang, Yang

ICCV 2019

10.21 EMNLP

Learning to Create Sentence Semantic Relation Graphs for Multi-document Summarization

Diego, Antognini, Boi, Faltings

EMNLP 2019

Dependency-guided Lstm-crf for Named Entity Recognition

Zhanming, Jie, Wei, Lu

EMNLP 2019

Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity

Penghui, Wei, Nan, Xu, Wenji, Mao

EMNLP 2019

Dialoguegcn: A Graph Convolutional Neural Network for Emotion Recognition in Conversation

Deepanway, Ghosal, Navonil, Majumder, Soujanya, Poria, Niyati, Chhaya, Alexander, Gelbukh

EMNLP 2019

Modeling Graph Structure in Transformer for Better Amr-to-text Generation

Jie, Zhu, Junhui, Li, Muhua, Zhu, Longhua, Qian, Min, Zhang, Guodong, Zhou

EMNLP 2019

Kagnet: Knowledge-aware Graph Networks for Commonsense Reasoning

Bill, Yuchen, Lin, Xinyue, Chen, Jamin, Chen, Xiang, Ren

EMNLP 2019

10.22 NeurIPS

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

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

NeurIPS 2019

Variational Graph Recurrent Neural Networks

Ehsan, Hajiramezanali, Arman, Hasanzadeh, Nick, Duffield, Krishna, R, Narayanan, Mingyuan, Zhou, Xiaoning, Qian

NeurIPS 2019

Social-bigat: Multimodal Trajectory Forecasting Using Bicycle-gan and Graph Attention Networks

Vineet, Kosaraju, Amir, Sadeghian, Roberto, Martín-Martín, Ian, Reid, S., Hamid, Rezatofighi, Silvio, Savarese

NeurIPS 2019

Probabilistic Logic Neural Networks for Reasoning

Meng, Qu, Jian, Tang

NeurIPS 2019

Quaternion Knowledge Graph Embeddings

Shuai, Zhang, Yi, Tay, Lina, Yao, Qi, Liu

NeurIPS 2019

Quantum Embedding of Knowledge for Reasoning

Dinesh, Garg, Santosh, K., Srivastava, Hima, Karanam

NeurIPS 2019

Multi-relational Poincaré Graph Embeddings

Ivana, Balaževic, Carl, Allen, Timothy, Hospedales

NeurIPS 2019

Dfnets: Spectral Cnns for Graphs with Feedback-looped Filters

Asiri, Wijesinghe, Qing, Wang

NeurIPS 2019

Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology

Nima, Dehmamy, Albert-László, Barabási, Rose, Yu

NeurIPS 2019

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

Jiaqi, Ma, Weijing, Tang, Ji, Zhu, Qiaozhu, Mei

NeurIPS 2019

Rethinking Kernel Methods for Node Representation Learning on Graphs

Yu, Tian, Long, Zhao, Xi, Peng, Dimitris, N., Metaxas

NeurIPS 2019

Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks

Sitao, Luan, Mingde, Zhao, Xiao-Wen, Chang, Doina, Precup

NeurIPS 2019

N-gram Graph: A Simple Unsupervised Representation for Molecules

Shengchao, Liu, Thevaa, Chandereng, Yingyu, Liang

NeurIPS 2019

Semantically-regularized Logic Graph Embeddings

Yaqi, Xie, Ziwei, Xu, Kuldeep, Meel, Mohan, S, Kankanhalli, Harold, Soh

NeurIPS 2019

Semi-implicit Graph Variational Auto-encoders

Arman, Hasanzadeh, Ehsan, Hajiramezanali, Nick, Duffield, Krishna, Narayanan, Mingyuan, Zhou, Xiaoning, Qian

NeurIPS 2019

D-vae: A Variational Autoencoder for Directed Acyclic Graphs

Muhan, Zhang, Shali, Jiang, Zhicheng, Cui, Roman, Garnett, Yixin, Chen

NeurIPS 2019

No Press Diplomacy: Modeling Multi-agent Gameplay

Philip, Paquette, Yuchen, Lu, Steven, Bocco, Max, O., Smith, Satya, Ortiz-Gagne, Jonathan, K., Kummerfeld, Satinder, Singh, Joelle, Pineau, Aaron, Courville

NeurIPS 2019

Approximation Ratios of Graph Neural Networks for Combinatorial Problems

Ryoma, Sato, Makoto, Yamada, Hisashi, Kashima

NeurIPS 2019

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

Maxime, Gasse, Didier, Chételat, Nicola, Ferroni, Laurent, Charlin, Andrea, Lodi

NeurIPS 2019

On Learning Paradigms for the Travelling Salesman Problem

Chaitanya, K., Joshi, Thomas, Laurent, Xavier, Bresson

NeurIPS 2019 Workshop

Learning to Propagate for Graph Meta-learning

Lu, Liu, Tianyi, Zhou, Guodong, Long, Jing, Jiang, Chengqi, Zhang

NeurIPS 2019

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

Jiaqi, Ma, Weijing, Tang, Ji, Zhu, Qiaozhu, Mei

NeurIPS 2019

Graph Normalizing Flows

Jenny, Liu, Aviral, Kumar, Jimmy, Ba, Jamie, Kiros, Kevin, Swersky

NeurIPS 2019

Conditional Structure Generation through Graph Variational Generative Adversarial Nets

Carl, Yang, Peiye, Zhuang, Wenhan, Shi, Alan, Luu, Pan, Li

NeurIPS 2019

Efficient Graph Generation with Graph Recurrent Attention Networks

Renjie, Liao, Yujia, Li, Yang, Song, Shenlong, Wang, Charlie, Nash, William, L., Hamilton, David, Duvenaud, Raquel, Urtasun, Richard, Zemel

NeurIPS 2019

11 2020

11.1 ICLR

Graphzoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding

Chenhui, Deng, Zhiqiang, Zhao, Yongyu, Wang, Zhiru, Zhang, Zhuo, Feng

ICLR 2020

Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning

Xiaoran, Xu, Wei, Feng, Yunsheng, Jiang, Xiaohui, Xie, Zhiqing, Sun, Zhi-Hong, Deng

ICLR 2020

Curvature Graph Network

Ze, Ye, Kin, Sum, Liu, Tengfei, Ma, Jie, Gao, Chao, Chen

ICLR 2020

Memory-based Graph Networks

Amir, hosein, Khasahmadi, Kaveh, Hassani, Parsa, Moradi, Leo, Lee, Quaid, Morris

ICLR 2020

Strategies for Pre-training Graph Neural Networks

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

ICLR 2020

Deep Graph Matching Consensus

Matthias, Fey, Jan, E., Lenssen, Christopher, Morris, Jonathan, Masci, Nils, M., Kriege

ICLR 2020

Few-shot Learning on Graphs via Super-classes Based on Graph Spectral Measures

Jatin, Chauhan, Deepak, Nathani, Manohar, Kaul

ICLR 2020

Automated Relational Meta-learning

Huaxiu, Yao, Xian, Wu, Zhiqiang, Tao, Yaliang, Li, Bolin, Ding, Ruirui, Li, Zhenhui, Li

ICLR 2020

Directional Message Passing for Molecular Graphs

Johannes, Klicpera, Janek, Groß, Stephan, Günnemann

ICLR 2020

Neural Execution of Graph Algorithms

Petar, Veličković, Rex, Ying, Matilde, Padovano, Raia, Hadsell, Charles, Blundell

ICLR 2020

Graphaf: a Flow-based Autoregressive Model for Molecular Graph Generation

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

ICLR 2020

12 Others

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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