Learning Regularization for Graph Inverse Problems
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Abstract (arXiv)
In recent years, Graph Neural Networks (GNNs) have been utilized for various applications ranging from drug discovery to network design and social networks. In many applications, it is impossible to observe some properties of the graph directly; instead, noisy and indirect measurements of these properties are available. These scenarios are coined as Graph Inverse Problems (GRIP). In this work, we introduce a framework leveraging GNNs to solve GRIPs. The framework is based on a combination of likelihood and prior terms, which are used to find a solution that fits the data while adhering to learned prior information. Specifically, we propose to combine recent deep learning techniques that were developed for inverse problems, together with GNN architectures, to formulate and solve GRIP. We study our approach on a number of representative problems that demonstrate the effectiveness of the framework.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 51 references that are in the map. First posted on arXiv 19 Aug 2024.
Builds on
21 of 51 references.
References · 21
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Graph Attention Networkscited by 8,854
- 2018How Powerful are Graph Neural Networks?cited by 4,355
- 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
- 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
- 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
- 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
- 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 431
- 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 384
- 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 290
- 2018Deep Neural Networks Motivated by Partial Differential Equationscited by 187
9 more
- 2017Solving ill-posed inverse problems using iterative deep neural networkscited by 145
- 2021PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equationscited by 106
- 2018Neural Proximal Gradient Descent for Compressive Imagingcited by 46
- 2021Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networkscited by 19
- 2020Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigmscited by 16
- 2023DRIP: Deep Regularizers for Inverse Problemscited by 7
- 2023Two-stage Denoising Diffusion Model for Source Localization in Graph Inverse Problemscited by 3
- 2024An Over Complete Deep Learning Method for Inverse Problemscited by 3
- 2024Source Localization for Cross Network Information Diffusioncited by 3
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.