Adapt, Agree, Aggregate: Semi-Supervised Ensemble Labeling for Graph Convolutional Networks

• URL: http://arxiv.org/abs/2503.17842v1
• Date: Sat, 22 Mar 2025 19:10:54 GMT
• Title: Adapt, Agree, Aggregate: Semi-Supervised Ensemble Labeling for Graph Convolutional Networks
• Authors: Maryam Abdolali, Romina Zakerian, Behnam Roshanfekr, Fardin Ayar, Mohammad Rahmati,
• Abstract summary: We propose a novel framework that combines ensemble learning with augmented graph structures.<n>Our approach harnesses the "wisdom of a diverse crowd", mitigating the challenges posed by noisy graph structures.
• Score: 3.6117547837781077
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a novel framework that combines ensemble learning with augmented graph structures to improve the performance and robustness of semi-supervised node classification in graphs. By creating multiple augmented views of the same graph, our approach harnesses the "wisdom of a diverse crowd", mitigating the challenges posed by noisy graph structures. Leveraging ensemble learning allows us to simultaneously achieve three key goals: adaptive confidence threshold selection based on model agreement, dynamic determination of the number of high-confidence samples for training, and robust extraction of pseudo-labels to mitigate confirmation bias. Our approach uniquely integrates adaptive ensemble consensus to flexibly guide pseudo-label extraction and sample selection, reducing the risks of error accumulation and improving robustness. Furthermore, the use of ensemble-driven consensus for pseudo-labeling captures subtle patterns that individual models often overlook, enabling the model to generalize better. Experiments on several real-world datasets demonstrate the effectiveness of our proposed method.

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