Guided Genetic Algorithm for Information Diffusion Problems

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Guided Genetic Algorithm for Information Diffusion Problems

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Guided Genetic Algorithm for Information Diffusion Problems

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GJ16-25694Y" target="_blank" >GJ16-25694Y: Mnohoparadigmatické algoritmy dolování z dat založené na vyhledávání, fuzzy technologiích a bio-inspirovaných výpočtech</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

ISBN

978-1-5090-6017-7

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

8

Strana od-do

1722-1729

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Rio de Janeiro

Datum konání akce

8. 7. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000451175500220