Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework
arXiv:2301.04318 · doi:10.1609/aaai.v37i4.25593
Abstract
Graph convolutional networks (GCNs) have been attracting widespread attentions due to their encouraging performance and powerful generalizations. However, few work provide a general view to interpret various GCNs and guide GCNs' designs. In this paper, by revisiting the original GCN, we induce an interpretable regularizer-centerd optimization framework, in which by building appropriate regularizers we can interpret most GCNs, such as APPNP, JKNet, DAGNN, and GNN-LF/HF. Further, under the proposed framework, we devise a dual-regularizer graph convolutional network (dubbed tsGCN) to capture topological and semantic structures from graph data. Since the derived learning rule for tsGCN contains an inverse of a large matrix and thus is time-consuming, we leverage the Woodbury matrix identity and low-rank approximation tricks to successfully decrease the high computational complexity of computing infinite-order graph convolutions. Extensive experiments on eight public datasets demonstrate that tsGCN achieves superior performance against quite a few state-of-the-art competitors w.r.t. classification tasks.
15 pages, 12 figures
References in corpus (10)
- Semi-Supervised Classification with Graph Convolutional Networks
- Simplifying Graph Convolutional Networks
- Towards Deeper Graph Neural Networks
- Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
- Interpreting and Unifying Graph Neural Networks with An Optimization Framework
- Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting
- Graph Neural Networks Inspired by Classical Iterative Algorithms
- A New Perspective on the Effects of Spectrum in Graph Neural Networks
- Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction
- Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning