Comparative Study between Kleinberg Algorithm and Biased Selection Algorithm for Construction of Small World Networks

Authors

  • Miguel Arcos Argudo Universidad Politécnica Salesiana del Ecuador, Grupo de Investigación en Inteligencia Artificial y Tecnología de Asistencia (GI-IATa)

DOI:

https://doi.org/10.13053/cys-21-2-2722

Keywords:

Biased selection, graph, Kleinberg, Markov chains, random walks, small worlds.

Abstract

Actually Small-World Networks is a very important topic, it is present in a lot of applications in our environment. A target of many algorithms is to establish methods to get that any node in a graph can establish a direct connection with a randomly “long-range neighbor”. This work is comparative study between two algorithms that get this target (Kleinberg and Biased Selection), I demonstrate by my experiments that both get the Kleinberg’s distribution. I conclude that the Kleinberg’s algorithm distribution maintains a probability directly proportional to Euclidian distance, and Biased Selection, although also maintains a probability directly proportional to Euclidian distance, allows that a node can get a farther node as “long-range neighbor” more frequently.

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Published

2017-06-30

Issue

Vol. 21 No. 2 (2017): Thematic issue: Authorship Analysis (Guest editors: M. Montes, L. Villaseñor)

Section

Articles

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