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

arXiv: https://arxiv.org/abs/2203.01093 · HTML: https://mlsanity.com/paper/2203.01093/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 41 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 23 of 23)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [GraphSAINT: Graph Sampling Based Inductive Learning Method](https://mlsanity.com/paper/1907.04931/) (2019)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [Unifying Graph Convolutional Neural Networks and Label Propagation](https://mlsanity.com/paper/2002.06755/) (2020)
- [Graph Transformation Policy Network for Chemical Reaction Prediction](https://mlsanity.com/paper/1812.09441/) (2018)
- [Active Learning for Graph Embedding](https://mlsanity.com/paper/1705.05085/) (2017)
- [Predicting Path Failure In Time-Evolving Graphs](https://mlsanity.com/paper/1905.03994/) (2019)
- [Graph Policy Network for Transferable Active Learning on Graphs](https://mlsanity.com/paper/2006.13463/) (2020)
- [OpenBox: A Generalized Black-box Optimization Service](https://mlsanity.com/paper/2106.00421/) (2021)
- [MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network](https://mlsanity.com/paper/1905.11013/) (2019)
- [On Graph Neural Networks versus Graph-Augmented MLPs](https://mlsanity.com/paper/2010.15116/) (2020)
- [Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization](https://mlsanity.com/paper/2108.00219/) (2021)
- [VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition](https://mlsanity.com/paper/2107.08861/) (2021)
- [RIM: Reliable Influence-based Active Learning on Graphs](https://mlsanity.com/paper/2110.14854/) (2021)
- [Exploring Representativeness and Informativeness for Active Learning](https://mlsanity.com/paper/1904.06685/) (2019)
- [SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs](https://mlsanity.com/paper/1908.08169/) (2019)

## Built on by (most cited first, 16 of 16)

- [Uncertainty in Graph Neural Networks: A Survey](https://mlsanity.com/paper/2403.07185/) (2024)
- [Data-centric Graph Learning: A Survey](https://mlsanity.com/paper/2310.04987/) (2023)
- [Towards Data-centric Graph Machine Learning: Review and Outlook](https://mlsanity.com/paper/2309.10979/) (2023)
- [Uncertainty for Active Learning on Graphs](https://mlsanity.com/paper/2405.01462/) (2024)
- [ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks](https://mlsanity.com/paper/2503.16693/) (2025)
- [Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs](https://mlsanity.com/paper/2304.11989/) (2023)
- [Robust Offline Active Learning on Graphs](https://mlsanity.com/paper/2408.07941/) (2024)
- [DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation](https://mlsanity.com/paper/2409.08946/) (2024)
- [Retrieving Minimal and Sufficient Reasoning Subgraphs with Graph Foundation Models for Path-aware GraphRAG](https://mlsanity.com/paper/2603.07179/) (2026)
- [Pose-based Tremor Type and Level Analysis for Parkinson's Disease from Video](https://mlsanity.com/paper/2312.13776/) (2023)
- [Active Learning for Graphs with Noisy Structures](https://mlsanity.com/paper/2402.02321/) (2024)
- [Reciprocal Learning](https://mlsanity.com/paper/2408.06257/) (2024)
- [Efficient Data Selection for Training Genomic Perturbation Models](https://mlsanity.com/paper/2503.14571/) (2025)
- [RANA: Robust Active Learning for Noisy Network Alignment](https://mlsanity.com/paper/2507.22434/) (2025)
- [An Approximation Algorithm for Graph Label Selection](https://mlsanity.com/paper/2605.18623/) (2026)
- [ALINC: Active Learning for Inductive Node Classification via Graph Sampling](https://mlsanity.com/paper/2606.04647/) (2026)

## 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.