A graph-based friend recommendation system using Genetic Algorithm

A social network is composed by communities of individuals or organizations that are connected by a common interest. Online social networking sites like Twitter, Facebook and Orkut are among the most visited sites in the Internet. Presently, there is a great interest in trying to understand the complexities of this type of network from both theoretical and applied point of view. The understanding of these social network graphs is important to improve the current social network systems, and also to develop new applications. Here, we propose a friend recommendation system for social network based on the topology of the network graphs. The topology of network that connects a user to his friends is examined and a local social network called Oro-Aro is used in the experiments. We developed an algorithm that analyses the sub-graph composed by a user and all the others connected people separately by three degree of separation. However, only users separated by two degree of separation are candidates to be suggested as a friend. The algorithm uses the patterns defined by their connections to find those users who have similar behavior as the root user. The recommendation mechanism was developed based on the characterization and analyses of the network formed by the user's friends and friends-of-friends (FOF).