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Related publications (31)

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

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

Mask Combination of Multi-Layer Graphs for Global Structure Inference

Pascal Frossard, Ntorina Thanou, Elif Vural, Eda Bayram

Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although ...

2020

Simple Realizability of Complete Abstract Topological Graphs Simplified

Jan Kyncl

An abstract topological graph (briefly an AT-graph) is a pair A = (G, X) where G = (V, E) is a graph and X. E2 is a set of pairs of its edges. The AT-graph A is simply realizable if G can be drawn in the plane so that each pair of edges from X crosses exac ...

SPRINGER2020

Who Started This Rumor? Quantifying the Natural Differential Privacy of Gossip Protocols

Rachid Guerraoui, Hadrien Hendrikx

Gossip protocols (also called rumor spreading or epidemic protocols) are widely used to disseminate information in massive peer-to-peer networks. These protocols are often claimed to guarantee privacy because of the uncertainty they introduce on the node t ...

Schloss Dagstuhl, Leibniz-Zentrum2020

Community-Aware Graph Signal Processing: Modularity Defines New Ways of Processing Graph Signals

Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton, Raphaël Pierre Liégeois, Miljan Petrovic

The emerging field of graph signal processing (GSP) allows one to transpose classical signal processing operations (e.g., filtering) to signals on graphs. The GSP framework is generally built upon the graph Laplacian, which plays a crucial role in studying ...

2020

Flooding And Diameter In General Weighted Random Graphs

Thomas Mountford, Jacques Saliba

In this paper we study first passage percolation on a random graph model, the configuration model. We first introduce the notions of weighted diameter, which is the maximum of the weighted lengths of all optimal paths between any two vertices in the graph, ...

CAMBRIDGE UNIV PRESS2020

Graph Learning Under Partial Observability

Ali H. Sayed, Augusto José Rabelo Almeida Santos

Many optimization, inference, and learning tasks can be accomplished efficiently by means of decentralized processing algorithms where the network topology (i.e., the graph) plays a critical role in enabling the interactions among neighboring nodes. There ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

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

The Schur-Erdos problem for sesmi-algebraic colorings

János Pach

We considerm-colorings of the edges of a complete graph, where each color class is defined semi-algebraically with bounded complexity. The casem= 2 was first studied by Alon et al., who applied this framework to obtain surprisingly strong Ramsey-type resul ...

HEBREW UNIV MAGNES PRESS2020

Space Efficient Approximation to Maximum Matching Size from Uniform Edge Samples

Mikhail Kapralov, Slobodan Mitrovic, Jakab Tardos, Ashkan Norouzi Fard

Given a source of iid samples of edges of an input graph G with n vertices and m edges, how many samples does one need to compute a constant factor approximation to the maximum matching size in G? Moreover, is it possible to obtain such an estimate in a sm ...

ASSOC COMPUTING MACHINERY2020

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