Concept
Graph rewriting
Publications associées (315)
Graph Representation Learning in Computational Pathology
Advances in scanning systems have enabled the digitization of pathology slides into Whole-Slide Images (WSIs), opening up opportunities to develop Computational Pathology (CompPath) methods for computer-aided cancer diagnosis and prognosis. CompPath has be ...
EPFL2022SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators
Nathanaël Perraudin, Andreas Loukas, Karolis Martinkus
We approach the graph generation problem from a spectral perspective by first generating the dominant parts of the graph Laplacian spectrum and then building a graph matching these eigenvalues and eigenvectors. Spectral conditioning allows for direct model ...
JMLR-JOURNAL MACHINE LEARNING RESEARCH2022The power of adaptivity in source identification with time queries on the path
Patrick Thiran, Gergely Odor, Victor Cyril L Lecomte
We study the problem of identifying the source of a stochastic diffusion process spreading on a graph based on the arrival times of the diffusion at a few queried nodes. In a graph , an unknown source node is drawn uniformly at random, ...
2022On the robustness of the metric dimension of grid graphs to adding a single edge
Patrick Thiran, Gergely Odor, Satvik Mehul Mashkaria
The metric dimension (MD) of a graph is a combinatorial notion capturing the minimum number of landmark nodes needed to distinguish every pair of nodes in the graph based on graph distance. We study how much the MD can increase if we add a single edge to t ...
2022Generalised Implicit Neural Representations
We consider the problem of learning implicit neural representations (INRs) for signals on non-Euclidean domains. In the Euclidean case, INRs are trained on a discrete sampling of a signal over a regular lattice. Here, we assume that the continuous signal e ...
2022Graph signal processing tailored for subgraph focus and community structure
Community structure in graph-modeled data appears in a range of disciplines that comprise network science. Its importance relies on the influence it bears on other properties of graphs such as resilience, or prediction of missing connections. Nevertheless, ...
EPFL2022Graph Neural Networks With Lifting-Based Adaptive Graph Wavelets
Pascal Frossard, Chenglin Li, Mingxing Xu
Spectral-based graph neural networks (SGNNs) have been attracting increasing attention in graph representation learning. However, existing SGNNs are limited in implementing graph filters with rigid transforms and cannot adapt to signals residing on graphs ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models
Negar Kiyavash, Ehsan Mokhtarian, Saber Salehkaleybar
We study experiment design for unique identification of the causal graph of a system where the graph may contain cycles. The presence of cycles in the structure introduces major challenges for experiment design as, unlike acyclic graphs, learning the skele ...
2022Distributed Graph Learning With Smooth Data Priors
Pascal Frossard, Mireille El Gheche, Isabela Cunha Maia Nobre
Graph learning is often a necessary step in processing or representing structured data, when the underlying graph is not given explicitly. Graph learning is generally performed centrally with a full knowledge of the graph signals, namely the data that live ...
IEEE2022Maintenance scheduling of manufacturing systems based on optimal price of the network
Goods can exhibit positive externalities impacting decisions of customers in social networks. Suppliers can integrate these externalities in their pricing strategies to increase their revenue. Besides optimizing the prize, suppliers also have to consider t ...
2022