Guided Genetic Algorithm for Information Diffusion Problems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10241706" target="_blank" >RIV/61989100:27240/18:10241706 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/8477835" target="_blank" >https://ieeexplore.ieee.org/document/8477835</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CEC.2018.8477835" target="_blank" >10.1109/CEC.2018.8477835</a>

Result language

angličtina

Original language name

Guided Genetic Algorithm for Information Diffusion Problems

Original language description

Information diffusion is a process that involves the propagation of an arbitrary signal (message) in an environment. In the area of social networks, it is often associated with influence maximization. Influence maximization consists in the search for an optimum set of k network nodes (seed sets) that trigger the activation of a maximum total number of remaining network nodes according to a chosen propagation model. It is an attractive research topic due to its well-known difficulty and many practical applications. Influence maximization can be used in various areas spanning from social network analysis and data mining to practical applications such as viral marketing and opinion making. Formally, it can be formulated as a subset selection problem. Because of the proven hardness of the influence maximization problem, many metaheuristic and evolutionary methods have been proposed to tackle it. This paper presents and evaluates a new genetic algorithm for influence maximization. It is based on a recent genetic algorithm for fixed-length subset selection and takes advantage of the knowledge of the environment. The evolutionary algorithm is in this approach executed with respect to network properties and the probability that vertices with chosen properties are selected is increased. The experiments show that this approach improves the results of the evolutionary procedure and leads to the discovery of better seed sets.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GJ16-25694Y" target="_blank" >GJ16-25694Y: Multi-paradigm data mining algorithms based on information retrieval, fuzzy, and bio-inspired methods</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

ISBN

978-1-5090-6017-7

ISSN

—

e-ISSN

neuvedeno

Number of pages

8

Pages from-to

1722-1729

Publisher name

IEEE

Place of publication

Piscataway

Event location

Rio de Janeiro

Event date

Jul 8, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000451175500220