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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

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

Joint Graph-based Depth Refinement and Normal Estimation

Pascal Frossard, Mireille El Gheche, Mattia Rossi

Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings. These scenes are characterized by a prevalence of human made structures, which in most of the cases, are either ...

2020

On clique coverings of complete multipartite graphs

Abhishek Methuku

A clique covering of a graph G is a set of cliques of G such that any edge of G is contained in one of these cliques, and the weight of a clique covering is the sum of the sizes of the cliques in it. The sigma clique cover number scc(G) of a graph G, is de ...

ELSEVIER2020

Smaller, Faster & Lighter KNN Graph Constructions

Rachid Guerraoui, Anne-Marie Kermarrec, Olivier Ruas

We propose GoldFinger, a new compact and fast-to-compute binary representation of datasets to approximate Jaccard’s index. We illustrate the effectiveness of GoldFinger on the emblematic big data problem of K-Nearest-Neighbor (KNN) graph construction and s ...

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

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

Non-Exponential Variations for Classical Results in First Passage Percolation

Jacques Saliba

We study in this thesis the asymptotic behavior of optimal paths on a random graph model, the configuration model, for which we assign continuous random positive weights on its edges. We start by describing the asymptotic behavior of the diameter and the f ...

EPFL2020

Revisiting Explicit Enumeration for Exact Synthesis

Heinz Riener

The problem of generating a minimal implementation of a given Boolean function is called exact synthesis. The parameter to be minimized is often the total number of gates used for the implementation. The exact synthesis engine is considered an essential to ...

IEEE COMPUTER SOC2020

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