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Found 14 papers, 5 papers with code
Graph learning under sparsity priors
If this is not possible, the data structure has to be inferred from the mere signal observations.
Reconstruction of Time-varying Graph Signals via Sobolev Smoothness
In the current work, we assume that the temporal differences of graph signals are smooth, and we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples.
Mask Combination of Multi-layer Graphs for Global Structure Inference
Structure inference is an important task for network data processing and analysis in data science.
Learning Laplacian Matrix in Smooth Graph Signal Representations
We show that the Gaussian prior leads to an efficient representation that favors the smoothness property of the graph signals.
Learning graphs from data: A signal representation perspective
The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis and visualization of structured data.
Learning heat diffusion graphs
Effective information analysis generally boils down to properly identifying the structure or geometry of the data, which is often represented by a graph.
Graph-based compression of dynamic 3D point cloud sequences
This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes.
Learning parametric dictionaries for graph signals
In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties -- the ability to adapt to specific signal data and a fast implementation of the dictionary.
Combining Anatomical and Functional Networks for Neuropathology Identification: A Case Study on Autism Spectrum Disorder
Finally, we use these new markers to train a decision tree, an interpretable classification scheme, which results in a final diagnosis aid model.
Graph signal processing for machine learning: A review and new perspectives
The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning.
node2coords: Graph Representation Learning with Wasserstein Barycenters
In the decoder, the node connectivity descriptors are reconstructed as Wasserstein barycenters of the graph structural patterns.
On the Stability of Graph Convolutional Neural Networks under Edge Rewiring
Graph neural networks are experiencing a surge of popularity within the machine learning community due to their ability to adapt to non-Euclidean domains and instil inductive biases.
Interpretable Stability Bounds for Spectral Graph Filters
In this paper, we study filter stability and provide a novel and interpretable upper bound on the change of filter output, where the bound is expressed in terms of the endpoint degrees of the deleted and newly added edges, as well as the spatial proximity of those edges.
Tertiary Lymphoid Structures Generation through Graph-based Diffusion
In particular, we show that the adopted graph diffusion model is able to accurately learn the distribution of cells in terms of their tertiary lymphoid structures (TLS) content, a well-established biomarker for evaluating the cancer progression in oncology research.
