Numerical Solution of the Inverse Problem for Diffusion-Logistic Model Arising in Online Social Networks

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

Аннотация

The information propagation in online social networks is characterized by a nonlinear partial differential equation with the Neumann boundary conditions and initial condition (source) that depends on the type of information and social network. The problem of source identification using additional measurements of the number of influenced users with a discrete distance at fixed times is numerically investigated. One way to solve the source problem for the diffusive-logistic model is to reduce it to the minimization least squares problem that is solved by a combination of the global particle swarm optimization and the local Nelder-Mead methods. Another way is to construct the function of the density of influenced users in space and time that describes additional measurements with high accuracy using a machine learning method named artificial neural networks. The results of numerical calculations for synthetic data show the accuracy of 99% of the source reconstruction. The neural networks approximate additional measurements with lower accuracy, but the approximation function satisfies the diffusive-logistic mathematical model. The novelty lies in the comparative analysis of the stochastic method for minimizing the misfit function based on the structure of the model, and the machine learning approach, which does not use the mathematical model while learning.

Язык оригиналаанглийский
Название основной публикацииMathematical Optimization Theory and Operations Research
Подзаголовок основной публикацииRecent Trends - 20th International Conference, MOTOR 2021, Revised Selected Papers
РедакторыAlexander Strekalovsky, Yury Kochetov, Tatiana Gruzdeva, Andrei Orlov
ИздательSpringer Science and Business Media Deutschland GmbH
Страницы444-459
Число страниц16
ISBN (печатное издание)9783030864323
DOI
СостояниеОпубликовано - 2021
Событие20th International Conference on Mathematical Optimization Theory and Operations Research , MOTOR 2021 - Virtual, Online
Продолжительность: 5 июл 202110 июл 2021

Серия публикаций

НазваниеCommunications in Computer and Information Science
Том1476 CCIS
ISSN (печатное издание)1865-0929
ISSN (электронное издание)1865-0937

Конференция

Конференция20th International Conference on Mathematical Optimization Theory and Operations Research , MOTOR 2021
ГородVirtual, Online
Период05.07.202110.07.2021

Предметные области OECD FOS+WOS

  • 1.01 МАТЕМАТИКА
  • 1.02 КОМПЬЮТЕРНЫЕ И ИНФОРМАЦИОННЫЕ НАУКИ

Fingerprint

Подробные сведения о темах исследования «Numerical Solution of the Inverse Problem for Diffusion-Logistic Model Arising in Online Social Networks». Вместе они формируют уникальный семантический отпечаток (fingerprint).

Цитировать