A reading paper list which is mainted daily
A collection of graph embedding, deep learning, recommendation, knowledge graph, heterogeneous graph papers with reference implementations
Created by gh-md-toc
2017- KDD - Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors’ Demonstration
2018 - https://github.com/hwwang55/DKNe Network for News Recommendation](https://github.com/hwwang55/DKN)
2018 - KDD - Deep Interest Network for Click-Through Rate Prediction
2018 - Recsys - Learning Consumer and Producer Embeddings for User-Generated Content Recommendation
2019 - ICML - Compositional Fairness Constraints for Graph Embeddings
2019 - KDD - NPA Neural News Recommendation with personalized attention
2013 -WSDM - News Recommendation via Hypergraph Learning: Encapsulation of User Behavior and News Content
2018 - CIKM - Weave & Rec : A Word Embedding based 3-D Convolutional Network for News Recommendation
2018 - IJCAI - A3NCF: An Adaptive Aspect Attention Model for Rating Prediction
2019 - WSDM - Social Attentional Memory Network: Modeling Aspect- and Friend-level Differences in Recommendation
2019 - WWW - Graph Neural Networks for Social Recommendation
2019 - CIKM - Spam Review Detection with Graph Convolutional Networks
2019 - EMNLP - Reviews Meet Graphs Enhancing User and Item Representations for recommendation with Hierachical Attentive Graph Neural Network
2019 - KDD - DAML Dual Attention Mutual Learning between Ratings and reviews
2018 - https://github.com/chenchongthu/NARREression with Review-level Explanations](https://github.com/chenchongthu/NARRE)
2020 - AAAI - Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation
2018 - CIKM - Sequential Recommendation Through Mixtures of Heterogeneous Item Relationships
2018 - https://github.com/vanzytay/WWW2018_LRML via Memory-based Attention for Collaborative Ranking](https://github.com/vanzytay/WWW2018_LRML)
2018 - AAAI - Explainable Recommendation Through Attentive Multi-View Learning
2018 - CIKM - RippleNet : Propagating User Preferences on the Knowledge Graph for Recommender Systems
2019 - AAAI - Explainable Reasoning over Knowledge Graphs for Recommendation
Min Zhang website (aim at explainable recommender system)
2019 - Representation Learning on Graphs: Methods and Applications
2019 - A Comprehensive Survey on Graph Neural Networks
2019 - Arxiv - Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
2019 - Arxiv - Feature-Attention Graph Convolutional Networks for Noise Resilient Learning
2019 - NIPS-GRL - Learnable Aggregator for GCN
2020 - ICLR - Geom-GCN: Geometric Graph Convolutional Networks
2019 - ICML - Disentangled Graph Convolutional Networks
2018 - ICML - Representation Learning on Graphs with Jumping Knowledge Networks
2019 - ICLR - Predict then Propagate: Graph Neural Networks meet Personalized PageRank
2019 - NIPS - Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
2019 - NIPS - Diffusion Improves Graph Learning
2019 - ICLR - Graph Wavelet Neural Network
2018 - AAAI - GraphGAN: Graph Representation Learning with Generative Adversarial Nets
2018 - CIKM - Semi-supervised Learning on Graphs with Generative Adversarial Nets
2019 - ICML - Simplifying Graph Convolutional Networks
2019 - ICLR - HOW POWERFUL ARE GRAPH NEURAL NETWORKS
2019 - ICLR - LanczosNet: Multi-Scale Deep Graph Convolutional Networks
2019 - AAAI - GeniePath: Graph Neural Networks with Adaptive Receptive Paths
2018 - ICLR - Graph Attention Networks
2018 - NIPS - Hierarchical Graph Representation Learning with Differentiable Pooling
2018 - NIPS - GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
2017 - NIPS - GraphSAGE: Inductive Representation Learning on Large Graphs
2018 - NIPS - Pitfalls of Graph Neural Network Evaluation
2017 - ICLR - SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
Stochastic Shared Embeddings Data-driven Regularization of Embedding Layers
2019 - Chemical - 2019 - Chemical - A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification
2020 - ICLR - PairNorm: Tackling Oversmoothing in GNNs
2020 - ICLR - DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
2020 - ICLR - Measuring and Improving the Use of Graph Information in Graph Neural Networks
2020 - ICLR - Characterize and Transfer Attention in Graph Neural Networks
2020 - AAAI - Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
2019 - SIGIR - Neural Graph Collaborative Filtering
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat Seng Chua
2019 - NIPS - Inductive Matrix Completion Based on Graph Neural Networks
该文章提出了一种基于图卷积网络的inductive,并且不使用辅助信息的矩阵补全方法。矩阵补全作为一个经典的问题,在许多领域有着应用,例如推荐系统。以往的方法比如低秩矩阵分解将矩阵分解成两个向量的乘积,他们往往是transductive的,不能够泛化到新的矩阵行和列上,KDD 2018的GCMC应用node-level的图卷积网络在bipartie graph上学习用户和物品特征表达,但其仍属于transductive的方法,而同为KDD 2018的pinsage虽然是inductive的模型,但是要依赖辅助信息如特征,并且特征的质量往往会影响模型的效果。本文提出一种基于图卷积网络的inductive矩阵补全方法,使得模型不依赖特征就可以泛化到新用户和新物品的矩阵补全方法。该方法主要由三步构成,包括了1.抽取包含目标用户和物品的sub-graph;2.为subgraph中不同的节点打上标签;3.graph-level的图卷积网络进行评分预测。最终作者在4个数据集上取得最好的表现效果,值得一提的是在movielens数据集上训练的模型在Douban数据集上进行测试,也能够超越一大部分baseline,显出该方法有着良好的transfer能力。
2018 - KDD - DeepInf: Social Influence Prediction with Deep Learning
Jiezhong Qiu , Jie Tang, et al
2018 - ICDM- Signed Graph Convolutional Network
yler Derr, Yao Ma, Jiliang Tang
2019 - AAAI - Graph Convolutional Networks for Text Classification
Liang Yao, Chengsheng Mao, Yuan Luo
2018 - KDD - Graph Convolutional Matrix Completion
Rianne van den Berg, Thomas N. Kipf, Max Welling
2018 - KDD - PinSage: Graph Convolutional Neural Networks for Web-Scale Recommender Systems
2020 - ICLR - Composition-based Multi-Relational Graph Convolutional Networks
2019 - AAAI - End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
2019 - AAAI - Modeling Relational Data with Graph Convolutional Networks
2018 - NIPS - SimplE Embedding for Link Prediction in Knowledge Graphs
2017 - AAAI - Convolutional 2D Knowledge Graph Embeddings
2013 - NIPS - Translating Embeddings for Modeling Multi-relational Data
2020 - ICLR - Hyper-SAGNN: a self-attention based graph neural network for hypergraphs
2019 - AAAI - Hypergraph Neural Networks
2018 - AAAI - Structural Deep Embedding for Hyper-Networks
2020 - AAAI - An Attention-based Graph Neural Network for Heterogeneous Structural Learning
2019 - NIPS - Graph Transformer Networks
2019 - https://github.com/Jhy1993/HANention Network](https://github.com/Jhy1993/HAN)
2019 - AAAI - Relation Structure-Aware Heterogeneous Information Network Embedding
2018 - CIKM - Are Meta-Paths Necessary ? Revisiting Heterogeneous Graph Embeddings
2018 - https://github.com/zyz282994112/GraphInception.giteous Information Networks](https://github.com/zyz282994112/GraphInception.git)
2018 - KDD - PME : Projected Metric Embedding on Heterogeneous Networks for Link Prediction
2017 - KDD - metapath2vec: Scalable Representation Learning for Heterogeneous Networks
2019 - CIKM - Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation
2019 - KDD - Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
2019 -EMNLP - Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification
2017 - KDD - Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks
2019 - AAAI - Cash-out User Detection based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism
2018 - KDD - Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model
2018 - IJCAI - Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks
2018 - A Survey on Network Embedding
2018 - A Tutorial on Network Embeddings
2017 - IJCAI - TransNet : Translation-Based Network Representation Learning for Social Relation Extraction
2019 - AAAI - TransConv: Relationship Embedding in Social Networks
2019 - ICLR - DEEP GRAPH INFOMAX
2018 IJCAI - ANRL: Attributed Network Representation Learning via Deep Neural Networks
2018 - NIPS - Recent Advances in Autoencoder-Based Representation Learning
Michael Tschannen, Olivier Bachem, Mario Lucic
2017 - NIPS - Bayesian GAN
2014 - ICML - Stochastic Gradient Hamiltonian Monte Carlo
2019 - MICA - Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation
2020 - AAAI - Uncertainty-Aware Multi-Shot Knowledge Distillation for Image-Based Object Re-Identification
2020 - CVPR - 3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training
2018 - CVPR - Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
2019 - CVPR - Striking the Right Balance with Uncertainty
2019 - thisis - Uncertainty Quantification in Deep Learning
2017 - NIPS - What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
2016 - ICML - Dropout as a Bayesian Approximation Representing Model Uncertainty in Deep Learning
2019 - ICCV - Probabilistic Face Embedding
2019 - ICCV - Robust Person Re-identification by Modelling Feature Uncertainty
2017 - ICML - On Calibration of Modern Neural Networks
2019 - NIPS - Variational Graph Convolutional Networks
2019 - ICML - Are Graph Neural Networks Miscalibrated?
2019 - NIPS - Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
2019 - NIPS - Uncertainty posters
2019 - ICLR - Modeling Uncertainty with Hedged Instance Embedding
2019 - NIPS - Practical Deep Learning with Bayesian Principles
2018 - NIPS - Multimodal Generative Models for Scalable Weakly-Supervised Learning
2014 - NIPS - Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions
2018 - ECCV - CBAM Convolutional Block Attention Module
提出了spatial 和 channel attention模块
2020 - CVPR - CONTRASTIVE REPRESENTATION DISTILLATION
2017 - NIPS - Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
2019 - NIPS - A Simple Baseline for Bayesian Uncertainty in Deep Learning
2020 - AISTATS - Confident Learning Estimating Uncertainty in Dataset Labels
PubMed Diabetes
Cora
other useful datasets link:
other useful dataset links