Learning Discrete Structures for Graph Neural Networks
Graph neural networks (GNNs) are a popular class of machine learning models whose major advantage is their ability to incorporate a sparse and discrete dependency structure between data points. Unfortunately, GNNs can only be used when such a graph-structure is available. In practice, however, real-world graphs are often noisy and incomplete or might not be available at all. With this work, we propose to jointly learn the graph structure and the parameters of graph convolutional networks (GCNs) by approximately solving a bilevel program that learns a discrete probability distribution on the edges of the graph. This allows one to apply GCNs not only in scenarios where the given graph is incomplete or corrupted but also in those where a graph is not available. We conduct a series of experiments that analyze the behavior of the proposed method and demonstrate that it outperforms related methods by a significant margin.
Code (2)
Tasks
Music Genre RecognitionNode ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation
Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidl…
Drug DiscoveryGraph GenerationGraph SamplingMolecular Graph GenerationGraphDF: A Discrete Flow Model for Molecular Graph Generation
We consider the problem of molecular graph generation using deep models. While graphs are discrete, most existing methods use continuous latent variables, resulting in inaccurate modeling of discrete graph structures. In…
Graph GenerationMolecular Graph GenerationDifferentiable Scaffolding Tree for Molecule Optimization
The structural design of functional molecules, also called molecular optimization, is an essential chemical science and engineering task with important applications, such as drug discovery. Deep generative models and com…
Combinatorial OptimizationDrug DiscoveryGraph Neural NetworkDifferentiable Scaffolding Tree for Molecular Optimization
The structural design of functional molecules, also called molecular optimization, is an essential chemical science and engineering task with important applications, such as drug discovery. Deep generative models and com…
Combinatorial OptimizationDrug DiscoveryGraph Neural NetworkA Simple Hypergraph Kernel Convolution based on Discounted Markov Diffusion Process
Kernels on discrete structures evaluate pairwise similarities between objects which capture semantics and inherent topology information. Existing kernels on discrete structures are only developed by topology information(…
Node ClassificationTransductive Learning