Papers › Robust Graph Clustering via Meta Weighting for Noisy Graphs

Robust Graph Clustering via Meta Weighting for Noisy Graphs

1 Nov 2023arXiv:2311.00322archive 2025-07-28

Hyeonsoo Jo, Fanchen Bu, Kijung Shin

How can we find meaningful clusters in a graph robustly against noise edges? Graph clustering (i.e., dividing nodes into groups of similar ones) is a fundamental problem in graph analysis with applications in various fields. Recent studies have demonstrated that graph neural network (GNN) based approaches yield promising results for graph clustering. However, we observe that their performance degenerates significantly on graphs with noise edges, which are prevalent in practice. In this work, we propose MetaGC for robust GNN-based graph clustering. MetaGC employs a decomposable clustering loss function, which can be rephrased as a sum of losses over node pairs. We add a learnable weight to each node pair, and MetaGC adaptively adjusts the weights of node pairs using meta-weighting so that the weights of meaningful node pairs increase and the weights of less-meaningful ones (e.g., noise edges) decrease. We show empirically that MetaGC learns weights as intended and consequently outperforms the state-of-the-art GNN-based competitors, even when they are equipped with separate denoising schemes, on five real-world graphs under varying levels of noise. Our code and datasets are available at https://github.com/HyeonsooJo/MetaGC.

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adarmic_adar hyeonsoojo/metagc/utils/data_utils.py official repository ran MIT (permissive) · 8df7821a89f8237c · report
add_noise_edges hyeonsoojo/metagc/utils/data_utils.py official repository ran MIT (permissive) · 972f0874d70166c2 · report
modularity hyeonsoojo/metagc/utils/metric.py official repository ran MIT (permissive) · 3152f573a8af402b · report
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Tasks

ClusteringDenoisingGraph ClusteringGraph Neural Network

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Graph Neural Network

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