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Related publications (32)

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Molecular hypergraph neural networks

Philippe Schwaller, Junwu Chen

Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as m ...

Aip Publishing2024

Hypergraph-Based Fast Distributed AC Power Flow Optimization

Colin Neil Jones, Yuning Jiang, Yingzhao Lian

This paper presents a novel distributed approach for solving AC power flow (PF) problems. The optimization problem is reformulated into a distributed form using a communication structure corresponding to a hypergraph, by which complex relationships between ...

IEEE2023

Equivariant Neural Architectures for Representing and Generating Graphs

Clément Arthur Yvon Vignac

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

EPFL2023

SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators

Andreas Loukas, Nathanaël Perraudin

We approach the graph generation problem from a spectral perspective by first generating the dominant parts of the graph Laplacian spectrum and then building a graph matching these eigenvalues and eigenvectors. Spectral conditioning allows for direct model ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Motif Cut Sparsifiers

Mikhail Kapralov, Jakab Tardos, Mikhail Makarov

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

Streaming and Matching Problems with Submodular Functions

Paritosh Garg

Submodular functions are a widely studied topic in theoretical computer science. They have found several applications both theoretical and practical in the fields of economics, combinatorial optimization and machine learning. More recently, there have also ...

EPFL2022

Space-Efficient Representations of Graphs

Jakab Tardos

With the increasing prevalence of massive datasets, it becomes important to design algorithmic techniques for dealing with scenarios where the input to be processed does not fit in the memory of a single machine. Many highly successful approaches have emer ...

EPFL2022

Tight Vector Bin Packing with Few Small Items via Fast Exact Matching in Multigraphs

Adam Teodor Polak, Alexandra Anna Lassota

We solve the Bin Packing problem in O^*(2^k) time, where k is the number of items less or equal to one third of the bin capacity. This parameter measures the distance from the polynomially solvable case of only large (i.e., greater than one third) items. O ...

Schloss Dagstuhl – Leibniz-Zentrum fur Informatik2022

Random walks and forbidden minors III: poly(d epsilon(-1))-time partition oracles for minor-free graph classes

Akash Kumar

Consider the family of bounded degree graphs in any minor-closed family (such as planar graphs). Let d be the degree bound and n be the number of vertices of such a graph. Graphs in these classes have hyperfinite decompositions, where, one removes a small ...

IEEE COMPUTER SOC2022

Towards Tight Bounds for Spectral Sparsification of Hypergraphs

Mikhail Kapralov, Jakab Tardos

Cut and spectral sparsification of graphs have numerous applications, including e.g. speeding up algorithms for cuts and Laplacian solvers. These powerful notions have recently been extended to hypergraphs, which are much richer and may offer new applicati ...

ASSOC COMPUTING MACHINERY2021

How to Match in Modern Computational Settings

Buddhima Ruwanmini Gamlath Gamlath Ralalage

This thesis focuses on the maximum matching problem in modern computational settings where the algorithms have to make decisions with partial information. First, we consider two stochastic models called query-commit and price-of-information where the algo ...

EPFL2021

Space Efficient Approximation to Maximum Matching Size from Uniform Edge Samples

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

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

Minimum-Error Classes for Matching Parts

Thomas Alois Weber

This paper examines the binning of two types of parts with random characteristics, so that a componentwise monotonic evaluation criterion exhibits a minimum deviation to a given target value over all possible realizations. The optimal matching classes are ...

2021

Dynamic Balanced Graph Partitioning

Andreas Loukas, Georg Stefan Schmid

This paper initiates the study of the classic balanced graph partitioning problem from an online perspective: Given an arbitrary sequence of pairwise communication requests between n nodes, with patterns that may change over time, the objective is to servi ...

SIAM Publications2020

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

Abhishek Methuku

Let c denote the largest constant such that every C-6-free graph G contains a bipartite and C-4-free subgraph having a fraction c of edges of G. Gyori, Kensell and Tompkins showed that 3/8

2020

Ramsey numbers of ordered graphs

Jan Kyncl

An ordered graph is a pair G = (G,

ELECTRONIC JOURNAL OF COMBINATORICS2020

A conformal dispersion relation: correlations from absorption

Din Carmi

We introduce the analog of Kramers-Kronig dispersion relations for correlators of four scalar operators in an arbitrary conformal field theory. The correlator is expressed as an integral over its "absorptive part", defined as a double discontinuity, times ...

Springer Nature2020

Saturation of Berge hypergraphs

Abhishek Methuku

Given a graph F, a hypergraph is a Berge-F if it can be obtained by expanding each edge in F to a hyperedge containing it. A hypergraph H is Berge-F-saturated if H does not contain a subhypergraph that is a Berge-F, but for any edge e is an element of E((H ...

Elsevier2019

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. ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2019

Linearity of saturation for Berge hypergraphs

Abhishek Methuku

For a graph F, we say a hypergraph H is a Berge-F if it can be obtained from F by replacing each edge of F with a hyperedge containing it. We say a hypergraph is Berge-F-saturated if it does not contain a Berge-F, but adding any hyperedge creates a copy of ...

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD2019