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Adaptive Majority Problems For Restricted Query Graphs And For Weighted Sets

Publications associées (121)

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Symbolic Algorithms for Token Swapping

Giovanni De Micheli, Mathias Soeken, Bruno Schmitt Antunes

We study different symbolic algorithms to solve two related reconfiguration problems on graphs: the token swapping problem and the permutation routing via matchings problem. Input to both problems is a connected graph with labeled vertices and a token in e ...

2020

Improved Ramsey-type results for comparability graphs

Istvan Tomon, Dániel József Korándi

Several discrete geometry problems are equivalent to estimating the size of the largest homogeneous sets in graphs that happen to be the union of few comparability graphs. An important observation for such results is that if G is an n-vertex graph that is ...

CAMBRIDGE UNIV PRESS2020

Graph Laplacian Mixture Model

Pascal Frossard, Hermina Petric Maretic

Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all avai ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

A Crossing Lemma for Multigraphs

János Pach

Let G be a drawing of a graph with n vertices and e > 4n edges, in which no two adjacent edges cross and any pair of independent edges cross at most once. According to the celebrated Crossing Lemma of Ajtai, Chvatal, Newborn, Szemeredi and Leighton, the nu ...

SPRINGER2020

Graph Signal Processing: Foundations and Emerging Directions [From the Guest Editors]

Dimitri Nestor Alice Van De Ville, Negar Kiyavash

The articles in this special section focus on graph signal processing. Generically, the networks that sustain our societies can be understood as complex systems formed by multiple nodes, where global network behavior arises from local interactions between ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

How hard is to distinguish graphs with graph neural networks?

Andreas Loukas

A hallmark of graph neural networks is their ability to distinguish the isomorphism class of their inputs. This study derives hardness results for the classification variant of graph isomorphism in the message-passing model (MPNN). MPNN encompasses the maj ...

2020

What graph neural networks cannot learn: depth vs width

Andreas Loukas

This paper studies the expressive power of graph neural networks falling within the message-passing framework (GNNmp). Two results are presented. First, GNNmp are shown to be Turing universal under sufficient conditions on their depth, width, node attribut ...

2020

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

Asyrnptotics for the Turan number of Berge-K-2,K-t

Abhishek Methuku

Let F be a graph. A hypergraph is called Berge-F if it can be obtained by replacing each edge in F by a hyperedge containing it. Let F be a family of graphs. The Turan number of the family Berge-F is the maximum possible number of edges in an r-uniform hyp ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2019

Generalized Turan Problems For Even Cycles

Abhishek Methuku

Given a graph H and a set of graphs F, let ex(n, H, F) denote the maximum possible number of copies of H in an T-free graph on n vertices. We investigate the function ex(n, H, F), when H and members of F are cycles. Let C-k denote the cycle of length k and ...

2019