Particle Competition and Cooperation for Robust Graph Convolutional Network Learning Under Label Noise
PCC+GCN refines noisy graph labels before training, slightly beating a baseline GCN with lower runtime.
PCC+GCN uses Particle Competition and Cooperation to detect suspicious labeled nodes and preserve, remove, or reassign their labels before Graph Convolutional Network training. The method was tested on ten NoisyGL datasets under Uniform, Pair, Random, and instance-dependent label noise, with hyperparameter analysis on Cora, CiteSeer, and PubMed. Under conventional noise it had the best average accuracy and rank, gaining 1.67 percentage points over a baseline GCN, and under instance-dependent noise it stayed competitive while being the fastest robust method on eight of ten datasets.
- PCC refines labels by preserving, removing, or reassigning suspicious nodes before GCN training.
- Tested on ten NoisyGL datasets under Uniform, Pair, Random, and instance-dependent noise.
- Average gain is 1.67 points over a baseline GCN across clean and conventional noise settings.
- Under instance-dependent noise it is the fastest robust method on eight of ten datasets.
From the original article
1 min read at arxiv.orgGraph Convolutional Networks (GCNs) are highly sensitive to label noise, since corrupted supervision can propagate through the graph and degrade learned node representations. This work proposes PCC+GCN, a hybrid framework that uses Particle Competition and Cooperation (PCC) as a graph-based label-refinement stage before GCN training.
By Fabricio Breve · arxiv.org
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