Maximum-Likelihood Network Reconstruction for SIS Processes is NP-Hard

07/23/2018
by   Bastian Prasse, et al.
0

The knowledge of the network topology is imperative to precisely describing the viral dynamics of an SIS epidemic process. In scenarios for which the network topology is unknown, one resorts to reconstructing the network from observing the viral state trace. This work focusses on the impact of the viral state observations on the computational complexity of the resulting network reconstruction problem. We propose a novel method of constructing a specific class of viral state traces from which the inference of the presence or absence of links is either easy or difficult. In particular, we use this construction to prove that the maximum-likelihood SIS network reconstruction is NP-hard. The NP-hardness holds for any adjacency matrix of a graph which is connected.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/02/2021

Some Inapproximability Results of MAP Inference and Exponentiated Determinantal Point Processes

We study the computational complexity of two hard problems on determinan...
research
06/24/2021

Kemeny ranking is NP-hard for 2-dimensional Euclidean preferences

The assumption that voters' preferences share some common structure is a...
research
07/31/2022

Identities in twisted Brauer monoids

We show that it is co-NP-hard to check whether a given semigroup identit...
research
09/13/2021

On explicit reductions between two purely algebraic problems: MQ and MLD

The Maximum Likelihood Decoding Problem (MLD) and the Multivariate Quadr...
research
08/29/2023

On k-Mer-Based and Maximum Likelihood Estimation Algorithms for Trace Reconstruction

The goal of the trace reconstruction problem is to recover a string x∈{0...
research
01/23/2013

Learning Polytrees

We consider the task of learning the maximum-likelihood polytree from da...
research
01/10/2013

Maximum Likelihood Bounded Tree-Width Markov Networks

Chow and Liu (1968) studied the problem of learning a maximumlikelihood ...

Please sign up or login with your details

Forgot password? Click here to reset