mlsanityData to 14 Oct 2026

Active Learning for Graph Embedding

arXiv 1705.05085 ↗First posted 15 May 2017cs.LG stat.MLcited by 54

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Abstract (arXiv)

Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information. In contrast to the graph structure data, the i.i.d. node embedding can be processed efficiently in terms of both time and space. Current semi-supervised graph embedding algorithms assume the labelled nodes are given, which may not be always true in the real world. While manually label all training data is inapplicable, how to select the subset of training data to label so as to maximize the graph analysis task performance is of great importance. This motivates our proposed active graph embedding (AGE) framework, in which we design a general active learning query strategy for any semi-supervised graph embedding algorithm. AGE selects the most informative nodes as the training labelled nodes based on the graphical information (i.e., node centrality) as well as the learnt node embedding (i.e., node classification uncertainty and node embedding representativeness). Different query criteria are combined with the time-sensitive parameters which shift the focus from graph based query criteria to embedding based criteria as the learning progresses. Experiments have been conducted on three public data sets and the results verified the effectiveness of each component of our query strategy and the power of combining them using time-sensitive parameters. Our code is available online at: https://github.com/vwz/AGE.

Key facts. Cited by 54 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 May 2017.

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Citing papers per year: 2021: 4; 2022: 9; 2023: 9; 2024: 14; 2025: 11; 2026: 7.

54 papers.

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  1. 2023Label-free Node Classification on Graphs with Large Language Models (LLMS)cited by 72
  2. 2024A Survey on Deep Active Learning: Recent Advances and New Frontierscited by 54
  3. 2021Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximizationcited by 28
  4. 2021RIM: Reliable Influence-based Active Learning on Graphscited by 23
  5. 2024Uncertainty in Graph Neural Networks: A Surveycited by 23
  6. 2023Data-centric Graph Learning: A Surveycited by 22
  7. 2021Graph Neural Networks: Methods, Applications, and Opportunitiescited by 16
  8. 2022Information Gain Propagation: a new way to Graph Active Learning with Soft Labelscited by 16
  9. 2022JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networkscited by 15
  10. 2023Towards Data-centric Graph Machine Learning: Review and Outlookcited by 15
  11. 2023Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networkscited by 14
  12. 2022Partition-Based Active Learning for Graph Neural Networkscited by 12
38 more
  1. 2024Uncertainty for Active Learning on Graphscited by 12
  2. 2024LEGO-Learn: Label-Efficient Graph Open-Set Learningcited by 10
  3. 2025CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisitioncited by 10
  4. 2024FastGAS: Fast Graph-based Annotation Selection for In-Context Learningcited by 8
  5. 2025Model Generalization on Text Attribute Graphs: Principles with Large Language Modelscited by 8
  6. 2025ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networkscited by 8
  7. 2024Multitask Active Learning for Graph Anomaly Detectioncited by 7
  8. 2022Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active Learningcited by 6
  9. 2024Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale Graphcited by 6
  10. 2025Few-Shot Graph Out-of-Distribution Detection with LLMscited by 6
  11. 2021Deep Unsupervised Active Learning on Learnable Graphscited by 4
  12. 2023A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Predictioncited by 4
  13. 2026GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedbackcited by 4
  14. 2022SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reductioncited by 3
  15. 2022Dissimilar Nodes Improve Graph Active Learningcited by 3
  16. 2023Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphscited by 3
  17. 2023Towards Label Position Bias in Graph Neural Networkscited by 3
  18. 2023Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learningcited by 3
  19. 2024On the Topology Awareness and Generalization Performance of Graph Neural Networkscited by 3
  20. 2024Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Modelscited by 3
  21. 2024Robust Offline Active Learning on Graphscited by 3
  22. 2022Tackling Provably Hard Representative Selection via Graph Neural Networkscited by 2
  23. 2024DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptationcited by 2
  24. 2025Language Models as Messengers: Enhancing Message Passing in Heterophilic Graph Learningcited by 2
  25. 2022Can I see an Example? Active Learning the Long Tail of Attributes and Relationscited by 1
  26. 2022ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learningcited by 1
  27. 2023DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classificationcited by 1
  28. 2024Active Learning for Graphs with Noisy Structurescited by 1
  29. 2024Class-Balanced and Reinforced Active Learning on Graphscited by 1
  30. 2025Shapley-Guided Utility Learning for Effective Graph Inference Data Valuationcited by 1
  31. 2026LoGIC: Budgeted Context Construction for Node-Level Graph In-Context Learning with Tabular Foundation Modelscited by 1
  32. 2024Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learningcited by 0
  33. 2025UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classificationcited by 0
  34. 2025Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignmentcited by 0
  35. 2025GRAIL: A Benchmark for GRaph ActIve Learning in Dynamic Sensing Environmentscited by 0
  36. 2025HIAL: A New Paradigm for Hypergraph Active Learning via Influence Maximizationcited by 0
  37. 2025RANA: Robust Active Learning for Noisy Network Alignmentcited by 0
  38. 2026Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMscited by 0

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