mlsanityData to 7 Oct 2026

Information Gain Propagation: a new way to Graph Active Learning with Soft Labels

arXiv 2203.01093 ↗First posted 2 Mar 2022cs.LGcited by 16

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Abstract (arXiv)

Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort. GNN-based Active Learning (AL) methods are proposed to improve the labeling efficiency by selecting the most valuable nodes to label. Existing methods assume an oracle can correctly categorize all the selected nodes and thus just focus on the node selection. However, such an exact labeling task is costly, especially when the categorization is out of the domain of individual expert (oracle). The paper goes further, presenting a soft-label approach to AL on GNNs. Our key innovations are: i) relaxed queries where a domain expert (oracle) only judges the correctness of the predicted labels (a binary question) rather than identifying the exact class (a multi-class question), and ii) new criteria of maximizing information gain propagation for active learner with relaxed queries and soft labels. Empirical studies on public datasets demonstrate that our method significantly outperforms the state-of-the-art GNN-based AL methods in terms of both accuracy and labeling cost.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 23 of its 41 references that are in the map. First posted on arXiv 2 Mar 2022.

Builds on

23 of 41 references.

References · 23

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2019Simplifying Graph Convolutional Networkscited by 1,470
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11 more
  1. 2019Predicting Path Failure In Time-Evolving Graphscited by 54
  2. 2017Active Learning for Graph Embeddingcited by 53
  3. 2020Graph Policy Network for Transferable Active Learning on Graphscited by 49
  4. 2021OpenBox: A Generalized Black-box Optimization Servicecited by 46
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  6. 2020On Graph Neural Networks versus Graph-Augmented MLPscited by 30
  7. 2021Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximizationcited by 28
  8. 2021VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decompositioncited by 25
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 6; 2025: 3; 2026: 3.

16 papers.

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  1. 2023Data-centric Graph Learning: A Surveycited by 22
  2. 2024Uncertainty in Graph Neural Networks: A Surveycited by 20
  3. 2023Towards Data-centric Graph Machine Learning: Review and Outlookcited by 15
  4. 2024Uncertainty for Active Learning on Graphscited by 11
  5. 2025ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networkscited by 8
  6. 2023Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphscited by 3
  7. 2024Robust Offline Active Learning on Graphscited by 3
  8. 2024DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptationcited by 2
  9. 2026Retrieving Minimal and Sufficient Reasoning Subgraphs with Graph Foundation Models for Path-aware GraphRAGcited by 2
  10. 2023Pose-based Tremor Type and Level Analysis for Parkinson's Disease from Videocited by 1
  11. 2024Active Learning for Graphs with Noisy Structurescited by 1
  12. 2024Reciprocal Learningcited by 0
4 more
  1. 2025Efficient Data Selection for Training Genomic Perturbation Modelscited by 0
  2. 2025RANA: Robust Active Learning for Noisy Network Alignmentcited by 0
  3. 2026An Approximation Algorithm for Graph Label Selectioncited by 0
  4. 2026ALINC: Active Learning for Inductive Node Classification via Graph Samplingcited by 0