Unifying Graph Convolutional Neural Networks and Label Propagation
This work addresses a theoretical gap in graph-based machine learning by integrating two message-passing algorithms, offering a task-oriented approach for researchers and practitioners in graph neural networks.
The paper tackles the problem of unifying Graph Convolutional Neural Networks (GCN) and Label Propagation (LPA) for node classification, proposing an end-to-end model that uses LPA as regularization to learn edge weights, resulting in improved classification accuracy over state-of-the-art GCN-based methods in experiments on real-world graphs.
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.