A collection of graph embedding, deep learning, recommendation, knowledge graph, heterogeneous graph papers with reference implementations

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Table of Contents

1. Recomendation
2. Graph
3. Bayesian Deep Learning
4. Datasets

• Explainable Reasoning over Knowledge Graphs for Recommendation

• Explainable Recommendation Through Attentive Multi-View Learning (AAAI 2018)

• RippleNet : Propagating User Preferences on the Knowledge Graph for Recommender Systems (CIKM 2018)

• Min Zhang website (aim at explainable recommender system)

Title	Conference	Author	Attachment
Survey
Survey: Representation Learning on Graphs: Methods and Applications		William L. Hamilton, Rex Ying, Jure Leskovec
A Comprehensive Survey on Graph Neural Networks		Zonghan Wu ,Philip S. Yu
Graph Theory
Representation Learning on Graphs with Jumping Knowledge Networks	ICML 2018	Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe,Ken-ichi Kawarabayashi, Stefanie Jegelka
Predict then Propagate: Graph Neural Networks meet Personalized PageRank	ICLR 2019	Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann	Pytorch and Tensorflow
Bayesian graph convolutional neural networks for semi-supervised classification	AAAI 2019	Jiatao Jiang, Zhen Cui, Chunyan Xu, Jian Yang
Graph Wavelet Neural Network	ICLR 2019	Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng	Pytorch, Tensorflow
GraphGAN: Graph Representation Learning with Generative Adversarial Nets	AAAI 2018	Hongwei Wang, Jia Wang, Jialin Wang,Miao Zhao,Weinan Zhang,Fuzheng Zhang Xing Xie, Minyi Guo	Tensorflow
Semi-supervised Learning on Graphs with Generative Adversarial Nets	CIKM 2018	Ming Ding，Jie Tang	Code
Simplifying Graph Convolutional Networks	ICML 2019 under review	Wu Felix, Zhang Tianyi, Souza, Amauri, Holanda de Fifty, Christopher, Yu, Tao, Weinberger, Kilian Q.	Pytorch
HOW POWERFUL ARE GRAPH NEURAL NETWORKS	ICLR 2019	Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka
LanczosNet: Multi-Scale Deep Graph Convolutional Networks	ICLR 2019	Renjie Liao, et al	code
GeniePath: Graph Neural Networks with Adaptive Receptive Paths	AAAI 2019	Le Song, Yuan Qi, et al
Graph Attention Networks	ICLR 2018	Petar Veliˇckovi´, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li`, Yoshua Bengio	Tensorflow
Hierarchical Graph Representation Learning with Differentiable Pooling	NIPS 2018	Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec	Code
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations	NIPS 2018	Zhilin Yang, Jake Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann LeCun
GraphSAGE: Inductive Representation Learning on Large Graphs	NIPS 2017		Code
Pitfalls of Graph Neural Network Evaluation	NIPS 2018	Shchur Oleksandr et al	Tensorflow & gnn bench mark
SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS	ICLR 2017	Thomas N. Kipf, Max Welling	Tensorflow
Graph Application
DeepInf: Social Influence Prediction with Deep Learning	KDD 2018	Jiezhong Qiu , [Jie Tang]， et al	Pytorch
Signed Graph Convolutional Network	ICDM 2018	Tyler Derr, Yao Ma, Jiliang Tang	Pytorch
Graph convolutional networks for text classification	AAAI 2019	Liang Yao, Chengsheng Mao, Yuan Luo	Tensorflow
Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations	WWW 2019	Hongyang Gao, Yongjun Chen, Shuiwang Ji
Graph Convolutional Matrix Completion	KDD 2018	Rianne van den Berg, Thomas N. Kipf, Max Welling	Tensorflow
PinSage: Graph Convolutional Neural Networks for Web-Scale Recommender Systems	KDD 2018	Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec
Knowledge Graph
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion	AAAI2019		Pytorch
Modeling Relational Data with Graph Convolutional Networks	ESWC 2018	Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling	Keras,Tensorflow
SimplE Embedding for Link Prediction in Knowledge Graphs	NIPS 2018	Seyed Mehran Kazemi, David Poole	Tensorflow
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems	CIKM 2018	Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, Minyi Guo	Tensorflow
DKN: Deep Knowledge-Aware Network for News Recommendation	WWW 2018	Hongwei Wang, Fuzheng Zhang, Xing Xie, Minyi Guo	Tensorflow
Convolutional 2D Knowledge Graph Embeddings	AAAI 2017	Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel	Pytorch
Translating Embeddings for Modeling Multi-relational Data	NIPS 2013	Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko	Code for TransE, TransH, TransR and PTransE
HyperGraph
Hypergraph Neural Networks	AAAI 2019	Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao	Pytorch
Structural Deep Embedding for Hyper-Networks	AAAI 2018	Ke Tu, Peng Cui, Xiao Wang, Fei Wang, Wenwu Zhu	Tensorflow
Modeling Multi-way Relations with Hypergraph Embedding	CIKM 2018	Chia-An Yu, Ching-Lun Tai, Tak-Shing Chan, Yi-Hsuan Yang	matlab
Heterogeneous Information Network
Cash-out User Detection based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism	Binbin Hu, Zhiqiang Zhang,Chuan Shi, Jun Zhou, Xiaolong Li, Yuan Qi	AAAI 2019	Code will be released ?
Heterogeneous Graph Attention Network	WWW 2019	Chuan Shi, Peng Cui et al	Pyotrch
Relation Structure-Aware Heterogeneous Information Network Embedding	AAAI 2019	Yuanfu Lu, Chuan Shi, Linmei Hu, Zhiyuan Liu	Pytorch
Are Meta-Paths Necessary ? Revisiting Heterogeneous Graph Embeddings	CIKM 2018	Rana Hussein	Request in email
Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model	KDD 2018	Binbin Hu, Chuan Shi, Wayne Xin Zhao, [Philip S. Yu]	Tensorflow&Keras,Data
PME : Projected Metric Embedding on Heterogeneous Networks for Link Prediction	KDD 2018	Hongxu Chen et al	Request in email
Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks	IJCAI 2018	Xiaotian Han, Chuan Shi, Senzhang Wang, [Philip S. Yu], Li Song	Tensorflow
Deep Collective Classification in Heterogeneous Information Networks	WWW 2018		Keras
Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks	KDD 2017	[Huan Zhao], anming Yao, Jianda Li, Yangqiu Song and Dik Lun Lee	Python
metapath2vec: Scalable Representation Learning for Heterogeneous Networks	KDD 2017	Yuxiao Dong	C++, Tensorflow
Hyperbolic embedding
Poincaré Embeddings for Learning Hierarchical Representations	NIPS 2017	Maximilian Nickel, Kiela Douwe	Pytorch
Hyperbolic Neural Networks	NIPS 2018	Octavian Eugen Ganea, Hofmann, Thomas	Tensorflow
Network Representation Learning (unsupervised)
A Survey on Network Embedding	2017	Peng Cui, Xiao Wang, Jian Pei, Wenwu Zhu
A Tutorial on Network Embeddings	2018	Haochen Chen, Bryan Perozzi, Rami Al-Rfou, Steven Skiena
TransNet : Translation-Based Network Representation Learning for Social Relation Extraction	IJCAI 2017	Cunchao Tu, Zhengyan, Maosong Sun	Tensorflow
TransConv: Relationship Embedding in Social Networks	AAAI 2019 oral
DEEP GRAPH INFOMAX	ICLR 2019	Petar Velickovi ˇ c´, William L. Hamilton, [Yoshua Bengio] et al	Pytorch
ANRL: Attributed Network Representation Learning via Deep Neural Networks	IJCAI 2018	Zhen Zhang, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, Can Wang	Tensosrflow

• Exploiting Relational Information in Social Networks using Geometric Deep Learning on Hypergraphs

  Other implement resource:

  • gated-graph-neural-network-samples
  • Graph-neural-networks jupyter tutorial
  • Deep Graph Library (DGL) Python package
  • pitafall: gnn model collection
  • pytorch_geometric
  • alimama euler framework
  • Liaojunjie: gnn model collection
  • node embedding from deepwalk to struc2vec
  • spektral

  Other reading materials:

  • Tsinghua University Graph papers reading list
  • gnn literature
  • MIA reading group
  • awesome-network-embedding
  • dynamic graph
  • zhihu link for graph
  • spatial-temporal graph
  • Technische Universität München

• PubMed Diabetes

  • The Pubmed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. The README file in the dataset provides more details.
  • Download Link:
    • https://linqs-data.soe.ucsc.edu/public/Pubmed-Diabetes.tgz
  • Related Papers:
    • Galileo Namata, et. al. "Query-driven Active Surveying for Collective Classification." MLG. 2012.

• Cora

  • The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words. The README file in the dataset provides more details.
  • Download Link:
    • https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz
  • Related Papers:
    • Qing Lu, and Lise Getoor. "Link-based classification." ICML, 2003.
    • Prithviraj Sen, et al. "Collective classification in network data." AI Magazine, 2008.

other useful datasets link:

• citation dataset
  • https://linqs.soe.ucsc.edu/data

• IMDB Datasets
  • MovieLens Latest Dataset which consists of 33,000 movies. And it contains four types of nodes: movie, director, actor and actress, connected by two types of relations/link: directed link and actor/actress staring link. Each movie is assigned with a set of class labels, indicating generes of the movie. For each movie, we extract a bag of words vector of all the plot summary about the movie as local features, which include 1000 words.
  • Download Link:
    • https://github.com/trangptm/Column_networks/tree/master/data
  • Related Papers:
    • T. Pham, et al. "Column networks for collective classification." In AAAI, 2017.
    • Zhang, Yizhou et al. "Deep Collective Classification in Heterogeneous Information Networks" In WWW, 2018.

other useful dataset links

• processed Datasets
  • https://github.com/librahu/Heterogeneous-Information-Network-Datasets-for-Recommendation-and-Network-Embedding/blob/master/README.md