/Awesome-Partial-Graph-Machine-Learning

A curated list for awesome partial graph machine learning resources.

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Awesome Partial Graph Machine Learning

Graph machine learning has been intensively studied and widely applied to various applications recently, such as social networks, knowledge graphs, recommender systems, etc. One underlying assumption commonly adopted by these methods is that all attributes of nodes are complete. However, in practice, this assumption may not hold due to 1) the absence of particular attributes and 2) the absence of all the attributes of specific nodes. Here we provide collections for partial graph machine learning literature.

Year 2024

Journal

  1. [TPAMI 2024] Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation [paper]
  2. [TNNLS 2024] Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network [paper]
  3. [TNNLS 2024] Multilevel Contrastive Graph Masked Autoencoders for Unsupervised Graph-Structure Learning [paper]
  4. [TNNLS 2024] Redundancy Is Not What You Need: An Embedding Fusion Graph Auto-Encoder for Self-Supervised Graph Representation Learning [paper]
  5. [TNNLS 2024] Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders [paper]
  6. [KBS 2024] HeGAE-AC: Heterogeneous Graph Auto-Encoder for Attribute Completion [paper]
  7. [KBS 2024] Attribute Imputation Autoencoders for Attribute-Missing Graphs [paper]
  8. [Information Fusion 2024] Multi-view Graph Imputation Network [paper]
  9. [TKDD 2024] ProtoMGAE: Prototype-Aware Masked Graph Auto-Encoder for Graph Representation Learning [paper]
  10. [BFG 2024] GAM-MDR: Probing MiRNA–Drug Resistance Using A Graph Autoencoder Based on Random Path Masking [paper]
  11. [TAI 2024] Incomplete Graph Learning via Partial Graph Convolutional Network [paper]
  12. [ESWA 2024] Higher Order Heterogeneous Graph Neural Network Based on Node Attribute Enhancement [paper]
  13. [TOMM 2024] SCAE: Structural Contrastive Auto-encoder for Incomplete Multi-view Representation Learning [paper]

Conference

  1. [WWW 2024] Masked Graph Autoencoder with Non-Discrete Bandwidths [paper|code]
  2. [AAAI 2024] Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding [paper]
  3. [AAAI 2024] GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with Masking [paper]
  4. [AAAI 2024] Rethinking Graph Masked Autoencoders through Alignment and Uniformity [paper]
  5. [AAAI 2024] Attribute-Missing Graph Clustering Network [paper]
  6. [AAAI 2024] Full-Body Motion Reconstruction with Sparse Sensing from Graph Perspective [paper]
  7. [IJCAI 2024] Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders [paper]
  8. [WSDM 2024] Incomplete Graph Learning via Attribute-Structure Decoupled Variational Auto-Encoder [paper]
  9. [WACV 2024] MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders [paper]
  10. [ICASSP 2024] Recovering Missing Node Features with Local Structure-Based Embeddings [paper]
  11. [ICASSP 2024] GLMAE: Graph Representation Learning Method Combining Generative Learning and Masking Autoencoder [paper]
  12. [ICASSP 2024] GFMAE: Self-Supervised GNN-Free Masked Autoencoders [paper]

Pre-Print Status

  1. [Arxiv 2024.01] Masked AutoEncoder for Graph Clustering without Pre-Defined Cluster Number k [paper]
  2. [Arxiv 2024.01] Progressive Distillation Based on Masked Generation Feature Method for Knowledge Graph Completion [paper]
  3. [Arxiv 2024.02] Graph-Based Forecasting with Missing Data through Spatiotemporal Downsampling [paper]
  4. [Arxiv 2024.02] Masked Attention is All You Need for Graphs [paper]
  5. [Arxiv 2024.02] UGMAE: A Unified Framework for Graph Masked Autoencoders [paper]
  6. [Arxiv 2024.05] Hi-GMAE: Hierarchical Graph Masked Autoencoders [paper]
  7. [Arxiv 2024.05] Masked Graph Transformer for Large-Scale Recommendation [paper]
  8. [Arxiv 2024.06] Reconstructing the Unseen: GRIOT for Attributed Graph Imputation with Optimal Transport [paper]
  9. [Arxiv 2024.06] MagiNet: Mask-Aware Graph Imputation Network for Incomplete Traffic Data [paper]

Year 2023

Journal

  1. [TPAMI 2023] GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation [paper]
  2. [TNNLS 2023] A Dual-Masked Deep Structural Clustering Network With Adaptive Bidirectional Information Delivery [paper]
  3. [TKDE 2023] Graph Neural Networks for Missing Value Classification in a Task-Driven Metric Space [paper]
  4. [TKDE 2023] DRGI: Deep Relational Graph Infomax for Knowledge Graph Completion [paper]
  5. [TMI 2023] Hybrid Graph Convolutional Network With Online Masked Autoencoder for Robust Multimodal Cancer Survival Prediction [paper]
  6. [TCSS 2023] CSAT: Contrastive Sampling-Aggregating Transformer for Community Detection in Attribute-Missing Networks [paper]
  7. [TBD 2023] Self-Attention Graph Convolution Residual Network for Traffic Data Completion [paper]
  8. [BIB 2023] SMG: Self-Supervised Masked Graph Learning for Cancer Gene Identification [paper]
  9. [BIB 2023] BatmanNet: Bi-Branch Masked Graph Transformer Autoencoder for Molecular Representation [paper]
  10. [KBS 2023] Dynamic Graph Convolutional Recurrent Imputation Network for Spatiotemporal Traffic Missing Data [paper]
  11. [Information Sciences 2023] Ensembled Masked Graph Autoencoders for Link Anomaly Detection in A Road Network Considering Spatiotemporal Features [paper]
  12. [Information Sciences 2023] HetReGAT-FC: Heterogeneous Residual Graph Attention Network via Feature Completion [paper]
  13. [Applied Intelligence 2023] Multi-Hop Question Answering over Incomplete Knowledge Graph with Abstract Conceptual Evidence [paper]
  14. [Remote Sensing 2023] Masked Graph Convolutional Network for Small Sample Classification of Hyperspectral Images [paper]
  15. [MIA 2023] Unsupervised Pre-Training of Graph Transformers on Patient Population Graphs [paper]
  16. [Methods 2023] Multi-Sample Dual-Decoder Graph Autoencoder [paper]
  17. [Nature Communications 2023] Chemistry-Intuitive Explanation of Graph Neural Networks for Molecular Property Prediction With Substructure Masking [paper]
  18. [KIS 2023] Node and Edge Dual-Masked Self-Supervised Graph Representation [paper]
  19. [DMKD 2023] Structure-Aware Decoupled Imputation Network for Multivariate Time Series [paper]

Conference

  1. [WWW 2023] SeeGera: Self-Supervised Semi-Implicit Graph Variational Auto-Encoders with Masking [paper]
  2. [WWW 2023] GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner [paper]
  3. [WWW 2023] Automated Spatio-Temporal Graph Contrastive Learning [paper]
  4. [WWW 2023] Automated Self-Supervised Learning for Recommendation [paper]
  5. [WWW 2023] Robust Graph Representation Learning for Local Corruption Recovery [paper]
  6. [ICLR 2023] Fair Attribute Completion on Graph with Missing Attributes [paper]
  7. [ICLR 2023] Confidence-Based Feature Imputation for Graphs with Partially Known Features [paper]
  8. [ICLR 2023] Learnable Topological Features for Phylogenetic Inference via Graph Neural Networks [paper]
  9. [ICLR 2023] Mole-BERT: Rethinking Pre-Training Graph Neural Networks for Molecules [paper]
  10. [ICLR 2023] ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion [paper]
  11. [AAAI 2023] T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation [paper]
  12. [AAAI 2023] Data Imputation with Iterative Graph Reconstruction [paper]
  13. [AAAI 2023] Heterogeneous Graph Masked Autoencoders [paper]
  14. [AAAI 2023] Revisiting Item Promotion in GNN-Based Collaborative Filtering: A Masked Targeted Topological Attack Perspective [paper]
  15. [AAAI 2023] Handling Missing Data via Max-Entropy Regularized Graph Autoencoder [paper]
  16. [KDD 2023] What’s Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders [paper]
  17. [SIGIR 2023] Graph Masked Autoencoder for Sequential Recommendation [paper]
  18. [SIGIR 2023] Distillation-Enhanced Graph Masked Autoencoders for Bundle Recommendation [paper]
  19. [VLDB 2023] Missing Value Imputation for Multi-Attribute Sensor Data Streams via Message Propagation [paper]
  20. [ICCV 2023] SkeletonMAE: Graph-Based Masked Autoencoder for Skeleton Sequence Pre-Training [paper]
  21. [MM 2023] Scene Graph Masked Variational Autoencoders for 3D Scene Generation [paper]
  22. [CVPR 2023] Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction [paper]
  23. [ICML 2023] Graph Neural Networks Can Recover the Hidden Features Solely from the Graph Structure [paper]
  24. [USENIX Security 2024] MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning [paper]
  25. [NeurIPS 2023] Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules [paper|code]
  26. [NeurIPSWorkshop 2023] Motif-Aware Attribute Masking for Molecular Graph Pre-Training [paper]
  27. [CIKM 2023] GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction [paper]
  28. [WSDM 2023] Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation [paper]
  29. [WSDM 2023] S2GAE: Self-Supervised Graph Autoencoders are Generalizable Learners with Graph Masking [paper]
  30. [PKDD 2023] Contrastive Learning-Based Imputation-Prediction Networks for In-Hospital Mortality Risk Modeling using EHRs [paper]
  31. [ICASSP 2023] Self-Supervised Guided Hypergraph Feature Propagation for Semi-Supervised Classification with Missing Node Features [paper]
  32. [ICONIP 2023] M3FGM: A Node Masking and Multi-Granularity Message Passing-Based Federated Graph Model for Spatial-Temporal Data Prediction [paper]
  33. [AISTATS 2023] EGG-GAE: Scalable Graph Neural Networks for Tabular Data Imputation [paper]
  34. [NLPCC 2023] RMGCN: Masked Graph Convolutional Networks for Relation-Aware Entity Alignment with Dangling Cases [paper]
  35. [ICEBE 2023] Motif Masking-Based Self-Supervised Learning For Molecule Graph Representation Learning [paper]
  36. [SIGSAC 2023] Vulnerability Intelligence Alignment via Masked Graph Attention Networks [paper]
  37. [ICCAI 2023] MGPose: A Graph Data Augmentation Framework for 3D Human Pose Estimation via Masking [paper]
  38. [KSEM 2023] Learning Graph Neural Networks on Feature-Missing Graphs [paper]
  39. [ESWC 2023] Two-View Graph Neural Networks for Knowledge Graph Completion [paper]
  40. [ICTAI 2023] GEDI: A Graph-based End-to-end Data Imputation Framework [paper]
  41. [ICDM 2023] Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing [paper]
  42. [KDD 2023] Learning Strong Graph Neural Networks with Weak Information [paper]
  43. [IAIC 2023] An Overview of Graph Data Missing Value Imputation [paper]

Pre-Print Status

  1. [Arxiv 2023.01] Generative Graph Neural Networks for Link Prediction [paper]
  2. [Arxiv 2023.01] AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network [paper]
  3. [Arxiv 2023.01] Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Network [paper]
  4. [Arxiv 2023.01] HAT-GAE: Self-Supervised Graph Auto-Encoders with Hierarchical Adaptive Masking and Trainable Corruption [paper]
  5. [Arxiv 2023.05] AmGCL: Feature Imputation of Attribute Missing Graph via Self-Supervised Contrastive Learning [paper]
  6. [Arxiv 2023.06] Masked Contrastive Graph Representation Learning for Age Estimation [paper]
  7. [Arxiv 2023.07] Deep Masked Graph Matching for Correspondence Identification in Collaborative Perception [paper]
  8. [Arxiv 2023.11] Better Fair than Sorry: Adversarial Missing Data Imputation for Fair GNNs [paper]
  9. [Arxiv 2023.11] Cross-View Graph Consistency Learning for Invariant Graph Representations [paper]
  10. [Arxiv 2023.11] Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning [paper]
  11. [Arxiv 2023.11] Leveraging Graph Diffusion Models for Network Refinement Tasks [paper]
  12. [Arxiv 2023.11] Self-Supervised Heterogeneous Graph Variational Autoencoders [paper]
  13. [Openview 2023] Active Sampling for Node Attribute Completion on Graphs [paper]
  14. [Openview 2023] Toward Generalizability of Graph-Based Imputation on Bio-Medical Missing Data [paper]

Year 2022

Journal

  1. [TPAMI 2022] Learning on Attribute-Missing Graphs [paper|code]
  2. [TKDE 2022] An Attribute-Aware Attentive GCN Model for Attribute Missing in Recommendation [paper]
  3. [TMM 2022] Latent Heterogeneous Graph Network for Incomplete Multi-View Learning [paper]
  4. [TCYB 2022] Amer: A New Attribute-Missing Network Embedding Approach [paper]
  5. [TNNLS 2022] Analyzing Heterogeneous Networks with Missing Attributes by Unsupervised Contrastive Learning [paper]
  6. [TCSVT 2022] Incomplete Multi-View Clustering via Cross-View Relation Transfer [paper]
  7. [TITS 2022] Traffic State Data Imputation: An Efficient Generating Method Based on the Graph Aggregator [paper]
  8. [NMI 2022] Molecular Contrastive Learning of Representations via Graph Neural Networks [paper]
  9. [PR 2022] Incomplete Multiview Nonnegative Representation Learning with Multiple Graphs [paper]
  10. [KBS 2022] Heterogeneous Graph Neural Network for Attribute Completion [paper]
  11. [Neurocomputing 2022] Infer-AVAE: An Attribute Inference Model Based on Adversarial Variational Autoencoder [paper]
  12. [Information Sciences 2022] Active Knowledge Graph Completion [paper]
  13. [BIB 2022] Attention-Wise Masked Graph Contrastive Learning for Predicting Molecular Property [paper]
  14. [WCMC 2022] A Higher-Order Motif-Based Spatiotemporal Graph Imputation Approach for Transportation Networks [paper]

Conference

  1. [ACL 2022] SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-Trained Language Models [paper]
  2. [ACL 2022] Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment [paper]
  3. [ACL 2022] CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion [paper]
  4. [KDD 2022] GraphMAE: Self-Supervised Masked Graph Autoencoders [paper]
  5. [KDD 2022] Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries [paper]
  6. [KDD 2022] CoRGi: Content-Rich Graph Neural Networks with Attention [paper|code]
  7. [KDD 2022] SMORE: Knowledge Graph Completion and Multi-Hop Reasoning in Massive Knowledge Graphs [paper|code]
  8. [KDD 2022] Accurate Node Feature Estimation with Structured Variational Graph Autoencoder [paper]
  9. [IJCAI 2022] Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion [paper]
  10. [IJCAI 2022] Initializing Then Refining: A Simple Graph Attribute Imputation Network [paper]
  11. [IJCAI 2022] Graph Masked Autoencoder Enhanced Predictor for Neural Architecture Search [paper]
  12. [WWW 2022] Trustworthy Knowledge Graph Completion Based on Multi-Sourced Noisy Data [paper|code]
  13. [WWW 2022] From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer [paper]
  14. [WWW 2022] Deep Partial Multiplex Network Embedding [paper]
  15. [SIGIR 2022] Re-Thinking Knowledge Graph Completion Evaluation from an Information Retrieval Perspective [paper]
  16. [SIGIR 2022] Neighbour Interaction Based Click-Through Rate Prediction via Graph-Masked Transformer [paper]
  17. [ICLR 2022] Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods [paper|code]
  18. [ICLR 2022] Neural Methods for Logical Reasoning Over Knowledge Graphs [paper]
  19. [ICLR 2022] Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks [paper]
  20. [ICML 2022] Self-Supervised Representation Learning via Latent Graph Prediction [paper]
  21. [NeurIPS 2022] Neural-Symbolic Entangled Framework for Complex Query Answering [paper]
  22. [NeurIPS 2022] On the Discrimination Risk of Mean Aggregation Feature Imputation in Graphs [paper]
  23. [NeurIPS 2022] Rethinking Knowledge Graph Evaluation Under the Open-World Assumption [paper]
  24. [NeurIPS 2022] Deep Bidirectional Language-Knowledge Graph Pretraining [paper]
  25. [NeurIPS 2022] Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations [paper]
  26. [NeurIPS 2022] How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders [paper]
  27. [NeurIPS Workshop 2022] Bi-Channel Masked Graph Autoencoders for Spatially Resolved Single-Cell Transcriptomics Data Imputation [paper]
  28. [CIKM 2022] I Know What You Do Not Know: Knowledge Graph Embedding via Co-Distillation Learning [paper]
  29. [CIKM 2022] Models and Benchmarks for Representation Learning of Partially Observed Subgraphs [paper]
  30. [CIKM 2022] MGMAE: Molecular Representation Learning by Reconstructing Heterogeneous Graphs with A High Mask Ratio [paper]
  31. [CIKM 2022] MentorGNN: Deriving Curriculum for Pre-Training GNNs [paper]
  32. [CIKM 2022] On Positional and Structural Node Features for Graph Neural Networks on Non-Attributed Graphs [paper]
  33. [ICDM 2022] Higher-Order Masked Graph Neural Networks for Traffic Flow Prediction [paper]
  34. [EMNLP 2022] Self-Supervised Graph Masking Pre-Training for Graph-to-Text Generation [paper]
  35. [COLING 2022] The Effectiveness of Masked Language Modeling and Adapters for Factual Knowledge Injection [paper]
  36. [ECML-PKDD 2022] Masked Graph Auto-Encoder Constrained Graph Pooling [paper]
  37. [LOG 2022] On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs with Missing Node Features [paper]

Pre-Print Status

  1. [Arxiv 2022.01] MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs [paper]
  2. [Arxiv 2022.02] Graph Masked Autoencoders with Transformers [paper]
  3. [Arxiv 2022.03] ACTIVE: Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering [paper]
  4. [Arxiv 2022.03] Gransformer: Transformer-Based Graph Generation [paper]
  5. [Arxiv 2022.04] Graph Auto-Encoders for Network Completion [paper]
  6. [Arxiv 2022.05] MaskGAE: Masked Graph Modeling Meets Graph Autoencoders [paper]
  7. [Arxiv 2022.06] Schema-Guided Event Graph Completion [paper]
  8. [Arxiv 2022.07] Cybersecurity Entity Alignment via Masked Graph Attention Networks [paper]
  9. [Arxiv 2022.09] DiP-GNN: Discriminative Pre-Training of Graph Neural Networks [paper]
  10. [Arxiv 2022.09] Knowledge Graph Completion with Pre-Trained Multimodal Transformer and Twins Negative Sampling [paper]
  11. [Arxiv 2022.10] Federated Graph-Based Networks with Shared Embedding [paper]
  12. [Openview 2022] MGMA: Mesh Graph Masked Autoencoders for Self-Supervised Learning on 3D Shape [paper]

Year 2021

Journal

  1. [Nature Communications 2021] Masked Graph Modeling for Molecule Generation [paper]
  2. [FGCS 2021] Graph Convolutional Networks for Graphs Containing Missing Features [paper|code]
  3. [FGCS 2021] Efficient Search Over Incomplete Knowledge Graphs in Binarized Embedding Space [paper]
  4. [TSP 2021] Community Detection and Matrix Completion With Social and Item Similarity Graphs [paper|code]
  5. [iScience 2021] Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks [paper]

Conference

  1. [WWW 2021] Heterogeneous Graph Neural Network via Attribute Completion [paper|code]
  2. [WWW 2021] Mask-GVAE: Blind Denoising Graphs via Partition [paper]
  3. [NeurIPS 2021] Subgraph Federated Learning with Missing Neighbor Generation [paper]
  4. [NeurIPS 2021] Multi-View Contrastive Graph Clustering [paper|code]
  5. [NeurIPS 2021] Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM) [paper]
  6. [NeurIPS 2021] INDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise Encoding [paper]
  7. [AAAI Workshop 2021] Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion [paper]
  8. [KDD 2021] Relational Message Passing for Knowledge Graph Completion [paper]
  9. [CIKM 2021] Inductive Matrix Completion Using Graph Autoencoder [paper|code]
  10. [ICASSP 2021] Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation [paper]
  11. [ICDCS 2021] Heterogeneous Spatio-Temporal Graph Convolution Network for Traffic Forecasting with Missing Values [paper]
  12. [ICANN 2021] Spatial-Temporal Traffic Data Imputation via Graph Attention Convolutional Network [paper]

Pre-Print Status

  1. [Arxiv 2021.02] Wasserstein Diffusion on Graphs with Missing Attributes [paper]
  2. [Arxiv 2021.06] Incomplete Graph Representation and Learning via Partial Graph Neural Networks [paper]
  3. [Arxiv 2021.06] Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-Supervised Pretraining [paper]
  4. [Arxiv 2021.09] Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns [paper]
  5. [Arxiv 2021.10] VICAUSE: Simultaneous Missing Value Imputation and Causal Discovery with Groups [paper]
  6. [Arxiv 2021.12] Siamese Attribute-Missing Graph Auto-Encoder [paper]
  7. [Arxiv 2021.12] Incomplete Knowledge Graph Alignment [paper]

Year 2020

Journal

  1. [Neural Networks 2020] Missing Data Imputation with Adversarially-Trained Graph Convolutional Networks [paper|code]
  2. [KBS 2020] GRL: Knowledge Graph Completion With GAN-Based Reinforcement Learning [paper]
  3. [TSIPN 2020] Efficient Graph Learning From Noisy and Incomplete Data [paper]

Conference

  1. [NeurIPS 2020] Handling Missing Data with Graph Representation Learning [paper|code]
  2. [NeurIPS 2020] Matrix Completion with Hierarchical Graph Side Information [paper]
  3. [ICLR 2020] Inductive Matrix Completion Based on Graph Neural Networks [paper|code]
  4. [KDD 2020] GPT-GNN: Generative Pre-Training of Graph Neural Networks [paper]
  5. [KDD 2020] HGMF: Heterogeneous Graph-based Fusion for Multimodal Data with Incompleteness [paper]
  6. [EMNLP 2020] TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion [paper|code]
  7. [EMNLP 2020] Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer [paper|code]
  8. [EMNLP 2020] MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning [paper]

Pre-Print Status

  1. [Arxiv 2020.01] Node Masking: Making Graph Neural Networks Generalize and Scale Better [paper]

Year 2019

Journal

  1. [KBS 2019] Adversarial Learning for Multi-View Network Embedding on Incomplete Graphs [paper]

Conference

  1. [ICDM 2019] Learning to Hash for Efficient Search over Incomplete Knowledge Graphs [paper|code]
  2. [IJCAI 2019] Masked Graph Convolutional Network [paper]
  3. [CVPR 2019] Masked Graph Attention Network for Person Re-Identification [paper]
  4. [AAAI 2019] Matrix Completion for Graph-Based Deep Semi-Supervised Learning [paper]
  5. [K-CAP 2019] Contextual Graph Attention for Answering Logical Queries over Incomplete Knowledge Graphs [paper|code]

Pre-Print Status

  1. [Arxiv 2019.07] Node Attribute Generation on Graphs [paper]

Year 2018

Conference

  1. [ECCV Workshop 2018] Incomplete Multi-View Clustering via Graph Regularized Matrix Factorization [paper|code]
  2. [RecSys 2018] Spectral Collaborative Filtering [paper|code]
  3. [ICDM 2018] SINE: Scalable Incomplete Network Embedding [paper]

Pre-Print Status

  1. [Arxiv 2018.11] Attributed Network Embedding for Incomplete Attributed Networks [paper]
  2. [Arxiv 2018.11] A Simple Yet Effective Baseline For Non-Attributed Graph Classification [paper]

Year 2017

Conference

  1. [NeurIPS 2017] Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks [paper|code]
  2. [CIKM 2017] From Properties to Links: Deep Network Embedding on Incomplete Graphs [paper]

Pre-Print Status

  1. [Arxiv 2017.06] Graph Convolutional Matrix Completion [paper|code]

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