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Unifying Graph Convolutional Neural Networks and Label Propagation

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arxiv 2002.06755 v1 pith:7XF5LMKO submitted 2020-02-17 cs.LG stat.ML

Hongwei Wang , Jure Leskovec This is my paper
classification cs.LGstat.ML
keywords nodelabelfeatureclassificationmodelgraphweightsattention
verification ladder T0 review T1 audit T2 compute T3 formal
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Label Propagation (LPA) and Graph Convolutional Neural Networks (GCN) are both message passing algorithms on graphs. Both solve the task of node classification but LPA propagates node label information across the edges of the graph, while GCN propagates and transforms node feature information. However, while conceptually similar, theoretical relation between LPA and GCN has not yet been investigated. Here we study the relationship between LPA and GCN in terms of two aspects: (1) feature/label smoothing where we analyze how the feature/label of one node is spread over its neighbors; And, (2) feature/label influence of how much the initial feature/label of one node influences the final feature/label of another node. Based on our theoretical analysis, we propose an end-to-end model that unifies GCN and LPA for node classification. In our unified model, edge weights are learnable, and the LPA serves as regularization to assist the GCN in learning proper edge weights that lead to improved classification performance. Our model can also be seen as learning attention weights based on node labels, which is more task-oriented than existing feature-based attention models. In a number of experiments on real-world graphs, our model shows superiority over state-of-the-art GCN-based methods in terms of node classification accuracy.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning to Execute Graph Algorithms Exactly with Graph Neural Networks

    cs.LG 2026-01 conditional novelty 6.0 of 10

    A GNN with an ensemble of MLPs can exactly execute any LOCAL-model graph algorithm after learning a polynomial-size set of local template instructions.

  2. ReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions

    cs.LG 2025-07 conditional novelty 6.0 of 10

    A reparameterized masked diffusion model with variational EM gives scalable structured node classification, matching or beating GNN, label propagation, and continuous diffusion baselines.

  3. SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding

    cs.LG 2025-06 conditional novelty 6.0 of 10

    SaGIF adds an independent similarity encoder, initialized from a fused feature-and-topology oracle, to regular GNNs and reports better individual fairness on six benchmark graphs.

  4. RANA: Robust Active Learning for Noisy Network Alignment

    cs.LG 2025-07 reject novelty 5.0 of 10

    An active learning method for network alignment that selects node pairs with a noise-aware confidence score and denoises labels via model self-labeling and twin node pair queries.

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