Link Prediction for Flow-Driven Spatial Networks
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Abstract (arXiv)
Link prediction algorithms aim to infer the existence of connections (or links) between nodes in network-structured data and are typically applied to refine the connectivity among nodes. In this work, we focus on link prediction for flow-driven spatial networks, which are embedded in a Euclidean space and relate to physical exchange and transportation processes (e.g., blood flow in vessels or traffic flow in road networks). To this end, we propose the Graph Attentive Vectors (GAV) link prediction framework. GAV models simplified dynamics of physical flow in spatial networks via an attentive, neighborhood-aware message-passing paradigm, updating vector embeddings in a constrained manner. We evaluate GAV on eight flow-driven spatial networks given by whole-brain vessel graphs and road networks. GAV demonstrates superior performances across all datasets and metrics and outperformed the state-of-the-art on the ogbl-vessel benchmark at the time of submission by 12% (98.38 vs. 87.98 AUC). All code is publicly available on GitHub.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 28 of its 67 references that are in the map. First posted on arXiv 25 Mar 2023.
Builds on
28 of 67 references.
References · 28
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Graph Attention Networkscited by 8,854
- 2020End-to-End Object Detection with Transformerscited by 7,384
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2018How Powerful are Graph Neural Networks?cited by 4,355
- 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
- 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
- 2019Simplifying Graph Convolutional Networkscited by 1,470
- 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
- 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
16 more
- 2018Link Prediction Based on Graph Neural Networkscited by 829
- 2021How Attentive are Graph Attention Networks?cited by 818
- 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 602
- 2020clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentationcited by 286
- 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
- 2017SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernelscited by 160
- 2020Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learningcited by 148
- 2020Line Graph Neural Networks for Link Predictioncited by 89
- 2022Relationformer: A Unified Framework for Image-to-Graph Generationcited by 67
- 2020LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attentioncited by 65
- 2021Object DGCNN: 3D Object Detection using Dynamic Graphscited by 58
- 2022Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graphcited by 42
- 2021To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernelscited by 39
- 2021Scalable Robust Graph and Feature Extraction for Arbitrary Vessel Networks in Large Volumetric Datasetscited by 17
- 2023SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learningcited by 8
- 2023Simplifying Subgraph Representation Learning for Scalable Link Predictioncited by 7
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