A bio-inspired algorithm for searching relationships in Social Networks

Nowadays the Social Networks are experiencing a growing importance. The reason of this is that they enable the information exchange among people, meeting people in the same field of work or establishing collaborations with other research groups. In order to manage social networks and to find people inside them, they are usually represented as graphs with persons as nodes and relationships between them as edges. Once this is done, establishing contact with anyone involves searching the chain of people to reach him/her, that is, the search of the path inside the graph which joins two nodes. In this paper, a new algorithm based on nature is proposed to realize this search: SoS-ACO (Sense of Smell - Ant Colony Optimization). This algorithm improves the classical ACO algorithm when it is applied in huge graphs.