Graph-Revised Convolutional Network
arXiv:1911.07123
Abstract
Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linkage patterns across graphs in various applications. As real-world graphs are often incomplete and noisy, treating them as ground-truth information, which is a common practice in most GCNs, unavoidably leads to sub-optimal solutions. Existing efforts for addressing this problem either involve an over-parameterized model which is difficult to scale, or simply re-weight observed edges without dealing with the missing-edge issue. This paper proposes a novel framework called Graph-Revised Convolutional Network (GRCN), which avoids both extremes. Specifically, a GCN-based graph revision module is introduced for predicting missing edges and revising edge weights w.r.t. downstream tasks via joint optimization. A theoretical analysis reveals the connection between GRCN and previous work on multigraph belief propagation. Experiments on six benchmark datasets show that GRCN consistently outperforms strong baseline methods by a large margin, especially when the original graphs are severely incomplete or the labeled instances for model training are highly sparse.
arXiv commentECML-PKDD 2020
References in corpus (14)
- Adam: A Method for Stochastic Optimization
- Emergence of scaling in random networks
- Semi-Supervised Classification with Graph Convolutional Networks
- Inductive Representation Learning on Large Graphs
- Spectral Networks and Locally Connected Networks on Graphs
- Predicting Missing Links via Local Information
- Deep Convolutional Networks on Graph-Structured Data
- Fast Graph Representation Learning with PyTorch Geometric
- Simplifying Graph Convolutional Networks
- Graph Convolutional Matrix Completion
- Pitfalls of Graph Neural Network Evaluation
- Dynamic Graph CNN for Learning on Point Clouds
- Revisiting Semi-Supervised Learning with Graph Embeddings
- Learning Discrete Structures for Graph Neural Networks