Data to 7 Oct 2026

Graph Geometry Interaction Learning

arXiv 2010.12135 ↗First posted 23 Oct 2020cs.LGcited by 55

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Abstract (arXiv)

While numerous approaches have been developed to embed graphs into either Euclidean or hyperbolic spaces, they do not fully utilize the information available in graphs, or lack the flexibility to model intrinsic complex graph geometry. To utilize the strength of both Euclidean and hyperbolic geometries, we develop a novel Geometry Interaction Learning (GIL) method for graphs, a well-suited and efficient alternative for learning abundant geometric properties in graph. GIL captures a more informative internal structural features with low dimensions while maintaining conformal invariance of each space. Furthermore, our method endows each node the freedom to determine the importance of each geometry space via a flexible dual feature interaction learning and probability assembling mechanism. Promising experimental results are presented for five benchmark datasets on node classification and link prediction tasks.

Key facts. Cited by 55 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 13; 2023: 12; 2024: 9; 2025: 10; 2026: 1.

55 papers.

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2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
2022Graph Neural Networks for Graphs with Heterophily: A Surveycited by 175
2021Hyperbolic Deep Neural Networks: A Surveycited by 138
2021Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Spacecited by 67
2021Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detectioncited by 67
2021Hyperbolic Busemann Learning with Ideal Prototypescited by 40
2022Hyperbolic Graph Neural Networks: A Review of Methods and Applicationscited by 40
2021Link Prediction with Persistent Homology: An Interactive Viewcited by 37
2022HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularizationcited by 34
2021Pseudo-Riemannian Graph Convolutional Networkscited by 30
2022On the Robustness of Graph Neural Diffusion to Topology Perturbationscited by 30
2023HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusioncited by 24

38 more

2022Graph Anomaly Detection with Graph Neural Networks: Current Status and Challengescited by 23
2024Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learningcited by 23
2023A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifoldcited by 21
2025Hyperbolic Deep Learning for Foundation Models: A Surveycited by 20
2024Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FRONDcited by 16
2022Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysiscited by 15
2023Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learningcited by 15
2024DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashingcited by 15
2021Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filteringcited by 14
2022kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learningcited by 14
2023Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networkscited by 12
2026Mano: Restriking Manifold Optimization for LLM Trainingcited by 12
2022Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classificationcited by 11
2023Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networkscited by 11
2021D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learningcited by 8
2022Random Laplacian Features for Learning with Hyperbolic Spacecited by 8
2024Distributed-Order Fractional Graph Operating Networkcited by 7
2025Spectro-Riemannian Graph Neural Networkscited by 5
2025Neural Variable-Order Fractional Differential Equation Networkscited by 5
2022AMCAD: Adaptive Mixed-Curvature Representation based Advertisement Retrieval Systemcited by 4
2023SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifoldscited by 4
2023Capacity Bounds for Hyperbolic Neural Network Representations of Latent Tree Structurescited by 4
2023DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional Networkscited by 4
2024Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spacescited by 3
2024Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filteringcited by 3
2025DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology Modelingcited by 3
2022Hyperbolic Graph Representation Learning: A Tutorialcited by 2
2023Node-Specific Space Selection via Localized Geometric Hyperbolicity in Graph Neural Networkscited by 2
2023FMGNN: Fused Manifold Graph Neural Networkcited by 2
2025A Geometric Perspective for High-Dimensional Multiplex Graphscited by 2
2025Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Sizecited by 2
2021Unit Ball Model for Embedding Hierarchical Structures in the Complex Hyperbolic Spacecited by 1
2022Geometry Contrastive Learning on Heterogeneous Graphscited by 1
2023Distributional Signals for Node Classification in Graph Neural Networkscited by 1
2024Full-Body Motion Reconstruction with Sparse Sensing from Graph Perspectivecited by 1
2024Manifold GCN: Diffusion-based Convolutional Neural Network for Manifold-valued Graphscited by 1
2022Generalized Laplacian Regularized Framelet Graph Neural Networkscited by 0
2023Node Embedding from Hamiltonian Information Propagation in Graph Neural Networkscited by 0

50 most cited of 55 All 55 (JSON)