mlsanityData to 7 Oct 2026

Multi-Channel Graph Neural Network for Entity Alignment

arXiv 1908.09898 ↗First posted 26 Aug 2019cs.CLcited by 86

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Abstract (arXiv)

Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented knowledge graph (KG) embeddings by robustly encoding two KGs via multiple channels. Each channel encodes KGs via different relation weighting schemes with respect to self-attention towards KG completion and cross-KG attention for pruning exclusive entities respectively, which are further combined via pooling techniques. Moreover, we also infer and transfer rule knowledge for completing two KGs consistently. MuGNN is expected to reconcile the structural differences of two KGs, and thus make better use of seed alignments. Extensive experiments on five publicly available datasets demonstrate our superior performance (5% Hits@1 up on average).

Key facts. Cited by 86 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Aug 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 18; 2022: 26; 2023: 18; 2024: 15; 2025: 5; 2026: 4.

86 papers.

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  1. 2021Search to aggregate neighborhood for graph neural networkcited by 48
  2. 2021Differentially Private Federated Knowledge Graphs Embeddingcited by 45
  3. 2021Boosting the Speed of Entity Alignment 10*: Dual Attention Matching Network with Normalized Hard Sample Miningcited by 37
  4. 2022Multi-modal Contrastive Representation Learning for Entity Alignmentcited by 37
  5. 2022SelfKG: Self-Supervised Entity Alignment in Knowledge Graphscited by 32
  6. 2021A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learningcited by 31
  7. 2022MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybridcited by 29
  8. 2021From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignmentcited by 26
  9. 2022Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignmentcited by 24
  10. 2023Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignmentcited by 23
  11. 2021Knowing the No-match: Entity Alignment with Dangling Casescited by 20
  12. 2022ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similaritiescited by 20
38 more
  1. 2023Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignmentcited by 20
  2. 2021Multi-modal Entity Alignment in Hyperbolic Spacecited by 19
  3. 2023Unsupervised Entity Alignment for Temporal Knowledge Graphscited by 19
  4. 2022Towards Sparsification of Graph Neural Networkscited by 18
  5. 2022LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagationcited by 18
  6. 2022Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphscited by 16
  7. 2022InducT-GCN: Inductive Graph Convolutional Networks for Text Classificationcited by 15
  8. 2022Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphscited by 14
  9. 2021Multilingual Knowledge Graph Completion with Joint Relation and Entity Alignmentcited by 12
  10. 2021ActiveEA: Active Learning for Neural Entity Alignmentcited by 11
  11. 2022ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classificationcited by 10
  12. 2021Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completioncited by 9
  13. 2021Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and Robustnesscited by 9
  14. 2022Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embeddingcited by 9
  15. 2022Informed Multi-context Entity Alignmentcited by 8
  16. 2023Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph Datasetscited by 8
  17. 2021Reinforcement Learning based Collective Entity Alignment with Adaptive Featurescited by 7
  18. 2022Interactive Contrastive Learning for Self-supervised Entity Alignmentcited by 7
  19. 2022Ensemble Semi-supervised Entity Alignment via Cycle-teachingcited by 7
  20. 2023Revisit and Outstrip Entity Alignment: A Perspective of Generative Modelscited by 7
  21. 2023AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Modelscited by 7
  22. 2023Deep Active Alignment of Knowledge Graph Entities and Schematacited by 6
  23. 2021GPS: A Policy-driven Sampling Approach for Graph Representation Learningcited by 5
  24. 2022Knowledge Graph Embedding Methods for Entity Alignment: An Experimental Reviewcited by 5
  25. 2023SEA: A Scalable Entity Alignment Systemcited by 5
  26. 2023Joint Pre-training and Local Re-training: Transferable Representation Learning on Multi-source Knowledge Graphscited by 5
  27. 2024ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignmentcited by 5
  28. 2024Uncertainty Management in the Construction of Knowledge Graphs: a Surveycited by 5
  29. 2022Understanding Graph Convolutional Networks for Text Classificationcited by 4
  30. 2022Dangling-Aware Entity Alignment with Mixed High-Order Proximitiescited by 4
  31. 2024SEG:Seeds-Enhanced Iterative Refinement Graph Neural Network for Entity Alignmentcited by 4
  32. 2024Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Casescited by 3
  33. 2021A Self-supervised Method for Entity Alignmentcited by 2
  34. 2022Jointly Learning Knowledge Embedding and Neighborhood Consensus with Relational Knowledge Distillation for Entity Alignmentcited by 2
  35. 2022Deep Reinforcement Learning for Entity Alignmentcited by 2
  36. 2022Guiding Neural Entity Alignment with Compatibilitycited by 2
  37. 2022Dependency-aware Self-training for Entity Alignmentcited by 2
  38. 2023i-Align: an interpretable knowledge graph alignment modelcited by 2

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