Publications associées à Morphisme de graphes

On 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 ...

2022

Distributed 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 ...

IEEE2022

Variants of Homomorphism Polynomials Complete for Algebraic Complexity Classes

Aditya Vardhan Varre

We present polynomial families complete for the well-studied algebraic complexity classes VF, VBP, VP, and VNP. The polynomial families are based on the homomorphism polynomials studied in the recent works of Durand et al. (2014) and Mahajan et al. (2018). ...

ASSOC COMPUTING MACHINERY2021

Efficient Streaming Subgraph Isomorphism with Graph Neural Networks

Karl Aberer, Quoc Viet Hung Nguyen, Chi Thang Duong, Trung-Dung Hoang

Queries to detect isomorphic subgraphs are important in graph-based data management. While the problem of subgraph isomorphism search has received considerable attention for the static setting of a single query, or a batch thereof, existing approaches do n ...

ASSOC COMPUTING MACHINERY2021

Computational pipeline to probe NaV1.7 gain-of-function variants in neuropathic painful syndromes

Stefano Zamuner, Margherita Marchi

Applications of machine learning and graph theory techniques to neuroscience have witnessed an increased interest in the last decade due to the large data availability and unprecedented technology developments. Their employment to investigate the effect of ...

NATURE RESEARCH2020

Differentially Private Release of Synthetic Graphs

Mikhail Kapralov, Marek Elias

We propose a (epsilon, delta)-differentially private mechanism that, given an input graph G with n vertices and m edges, in polynomial time generates a synthetic graph G' approximating all cuts of the input graph up to an additive error of O (root mn/epsil ...

ASSOC COMPUTING MACHINERY2020

Learning Graph Influence From Social Interactions

Ali H. Sayed, Virginia Bordignon, Augusto José Rabelo Almeida Santos

In social learning, agents form their opinions or beliefs about certain hypotheses by exchanging local information. This work considers the recent paradigm of weak graphs, where the network is partitioned into sending and receiving components, with the for ...

IEEE2020

Graph Coarsening with Preserved Spectral Properties

Andreas Loukas

In graph coarsening, one aims to produce a coarse graph of reduced size while preserving important graph properties. However, as there is no consensus on which specific graph properties should be preserved by coarse graphs, measuring the differences betwee ...

ADDISON-WESLEY PUBL CO2020

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Morphisme de graphes

On the robustness of the metric dimension of grid graphs to adding a single edge

Distributed Graph Learning With Smooth Data Priors

Variants of Homomorphism Polynomials Complete for Algebraic Complexity Classes

Efficient Streaming Subgraph Isomorphism with Graph Neural Networks

Computational pipeline to probe NaV1.7 gain-of-function variants in neuropathic painful syndromes

Differentially Private Release of Synthetic Graphs

Learning Graph Influence From Social Interactions

On subgraphs of C-2k-free graphs and a problem of Kuhn and Osthus

Graph Coarsening with Preserved Spectral Properties

On the optimal sampling design for the Graph Total Variation decoder: recovering piecewise-constant graph signals from a few vertex measurements

Graph Chatbot

Chattez avec Graph Search

Variants of Homomorphism Polynomials Complete for Algebraic Complexity Classes

Differentially Private Release of Synthetic Graphs

Distributed Graph Learning With Smooth Data Priors

On subgraphs of C-2k-free graphs and a problem of Kuhn and Osthus

Computational pipeline to probe NaV1.7 gain-of-function variants in neuropathic painful syndromes

Efficient Streaming Subgraph Isomorphism with Graph Neural Networks

On the robustness of the metric dimension of grid graphs to adding a single edge

On the optimal sampling design for the Graph Total Variation decoder: recovering piecewise-constant graph signals from a few vertex measurements

Learning Graph Influence From Social Interactions

Graph Coarsening with Preserved Spectral Properties