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.
Translated title of the contribution | Estudio comparativo entre el algoritmo de Kleinberg y el algoritmo de selección sesgada para la construcción de pequeñas redes mundiales |
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Original language | English |
Pages (from-to) | 325-336 |
Number of pages | 12 |
Journal | Computacion y Sistemas |
DOIs | |
State | Published - 1 Jan 2017 |