Publications related to Pseudoforest

Scalable maximal subgraph mining with backbone-preserving graph convolutions

Karl Aberer, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Thành Tâm Nguyên

Maximal subgraph mining is increasingly important in various domains, including bioinformatics, genomics, and chemistry, as it helps identify common characteristics among a set of graphs and enables their classification into different categories. Existing ...

ELSEVIER SCIENCE INC2023

Motif Cut Sparsifiers

Mikhail Kapralov, Mikhail Makarov, Jakab Tardos

A motif is a frequently occurring subgraph of a given directed or undirected graph G (Milo et al.). Motifs capture higher order organizational structure of G beyond edge relationships, and, therefore, have found wide applications such as in graph clusterin ...

IEEE COMPUTER SOC2022

Thrackles: An improved upper bound

János Pach, Radoslav Fulek

A thrackle is a graph drawn in the plane so that every pair of its edges meet exactly once: either at a common end vertex or in a proper crossing. We prove that any thrackle of n vertices has at most 1.3984n edges. Quasi-thrackles are defined similarly, ex ...

ELSEVIER SCIENCE BV2019

Time-resolved analysis of dynamic graphs: an extended Slepian design

Dimitri Nestor Alice Van De Ville, Raphaël Pierre Liégeois, Ibrahim Merad

Graphs are extensively used to represent networked data. In many applications, especially when considering large datasets, it is a desirable feature to focus the analysis onto specific subgraphs of interest. Slepian theory and its extension to graphs allow ...

SPIE-INT SOC OPTICAL ENGINEERING2019

New Notions and Constructions of Sparsification for Graphs and Hypergraphs

Ola Nils Anders Svensson

A sparsifier of a graph G (Bencztir and Karger; Spielman and Teng) is a sparse weighted subgraph (G) over tilde that approximately retains the same cut structure of G. For general graphs, non-trivial sparsification is possible only by using weighted graphs ...

IEEE COMPUTER SOC2019

Monopulse Beam Synthesis Using a Sparse Single Layer of Weights

Semin Kwak

A conventional monopulse radar system uses three beams, namely, sum beam, elevation difference beam, and azimuth difference beam, which require different layers of weights to synthesize each beam independently. Since the multilayer structure increases the ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Generating and identifying functional subnetworks within structural networks

Henry Markram, Kathryn Hess Bellwald, Michael Reimann, Ran Levi, Max Christian Nolte

In one aspect, a method includes generating a functional subgraph of a network from a structural graph of the network. The structural graph comprises a set of vertices and structural connections between the vertices. Generating the functional subgraph incl ...

2018

Graph Heat Mixture Model Learning

Pascal Frossard, Mireille El Gheche, Hermina Petric Maretic

Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis. However, most of the available state-of-the-art methods focus on scenarios where all avai ...

IEEE2018

Approximation algorithms for geometric dispersion

Alfonso Bolívar Cevallos Manzano

The most basic form of the max-sum dispersion problem (MSD) is as follows: given n points in R^q and an integer k, select a set of k points such that the sum of the pairwise distances within the set is maximal. This is a prominent diversity problem, with w ...

EPFL2016

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Pseudoforest

Scalable maximal subgraph mining with backbone-preserving graph convolutions

Motif Cut Sparsifiers

Thrackles: An improved upper bound

Time-resolved analysis of dynamic graphs: an extended Slepian design

New Notions and Constructions of Sparsification for Graphs and Hypergraphs

Monopulse Beam Synthesis Using a Sparse Single Layer of Weights

Generating and identifying functional subnetworks within structural networks

Graph Heat Mixture Model Learning

Optimal Approximation for Submodular and Supermodular Optimization with Bounded Curvature

Approximation algorithms for geometric dispersion

Graph Chatbot

Chat with Graph Search

Scalable maximal subgraph mining with backbone-preserving graph convolutions

Motif Cut Sparsifiers

Thrackles: An improved upper bound

Approximation algorithms for geometric dispersion

Monopulse Beam Synthesis Using a Sparse Single Layer of Weights

New Notions and Constructions of Sparsification for Graphs and Hypergraphs

Generating and identifying functional subnetworks within structural networks

Optimal Approximation for Submodular and Supermodular Optimization with Bounded Curvature

Time-resolved analysis of dynamic graphs: an extended Slepian design

Graph Heat Mixture Model Learning