mlsanityData to 14 Oct 2026

RIM: Reliable Influence-based Active Learning on Graphs

arXiv 2110.14854 ↗First posted 28 Oct 2021cs.LGcited by 23

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Abstract (arXiv)

Message passing is the core of most graph models such as Graph Convolutional Network (GCN) and Label Propagation (LP), which usually require a large number of clean labeled data to smooth out the neighborhood over the graph. However, the labeling process can be tedious, costly, and error-prone in practice. In this paper, we propose to unify active learning (AL) and message passing towards minimizing labeling costs, e.g., making use of few and unreliable labels that can be obtained cheaply. We make two contributions towards that end. First, we open up a perspective by drawing a connection between AL enforcing message passing and social influence maximization, ensuring that the selected samples effectively improve the model performance. Second, we propose an extension to the influence model that incorporates an explicit quality factor to model label noise. In this way, we derive a fundamentally new AL selection criterion for GCN and LP--reliable influence maximization (RIM)--by considering quantity and quality of influence simultaneously. Empirical studies on public datasets show that RIM significantly outperforms current AL methods in terms of accuracy and efficiency.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 19 of its 59 references that are in the map. First posted on arXiv 28 Oct 2021.

Builds on

19 of 59 references.

References · 19

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  5. 2019Simplifying Graph Convolutional Networkscited by 1,477
  6. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 877
  7. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  8. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  9. 2018Deep Learning on Graphs: A Surveycited by 393
  10. 2020A Survey of Label-noise Representation Learning: Past, Present and Futurecited by 122
  11. 2020Unifying Graph Convolutional Neural Networks and Label Propagationcited by 98
  12. 2017Active Learning for Graph Embeddingcited by 54
7 more
  1. 2020Graph Policy Network for Transferable Active Learning on Graphscited by 50
  2. 2020On the Equivalence of Decoupled Graph Convolution Network and Label Propagationcited by 49
  3. 2021OpenBox: A Generalized Black-box Optimization Servicecited by 46
  4. 2021Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximizationcited by 28
  5. 2015Active Learning from Weak and Strong Labelerscited by 22
  6. 2015S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classificationcited by 20
  7. 2020Exploiting Context for Robustness to Label Noise in Active Learningcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 5; 2024: 4; 2025: 7; 2026: 4.

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  1. 2022Training Data Influence Analysis and Estimation: A Surveycited by 135
  2. 2023Label-free Node Classification on Graphs with Large Language Models (LLMS)cited by 72
  3. 2022Information Gain Propagation: a new way to Graph Active Learning with Soft Labelscited by 16
  4. 2023Towards Data-centric Graph Machine Learning: Review and Outlookcited by 15
  5. 2023Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networkscited by 9
  6. 2025Model Generalization on Text Attribute Graphs: Principles with Large Language Modelscited by 8
  7. 2024Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphscited by 6
  8. 2026GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedbackcited by 4
  9. 2023Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphscited by 3
  10. 2023Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learningcited by 3
  11. 2024Robust Offline Active Learning on Graphscited by 3
  12. 2025Toward Scalable Graph Unlearning: A Node Influence Maximization based Approachcited by 2
11 more
  1. 2025Language Models as Messengers: Enhancing Message Passing in Heterophilic Graph Learningcited by 2
  2. 2024Active Learning for Graphs with Noisy Structurescited by 1
  3. 2025Shapley-Guided Utility Learning for Effective Graph Inference Data Valuationcited by 1
  4. 2026InfGraND: An Influence-Guided GNN-to-MLP Knowledge Distillationcited by 1
  5. 2021K-Core Decomposition on Super Large Graphs with Limited Resourcescited by 0
  6. 2024Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learningcited by 0
  7. 2025Efficient Data Selection for Training Genomic Perturbation Modelscited by 0
  8. 2025HIAL: A New Paradigm for Hypergraph Active Learning via Influence Maximizationcited by 0
  9. 2025RANA: Robust Active Learning for Noisy Network Alignmentcited by 0
  10. 2026Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMscited by 0
  11. 2026Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teachingcited by 0