Abstract
This paper proposes an approach which aims at improving information access (skills, services) in large networks. It proposes a self-organized multiagent analysis of the problem allowing to reduce the number of messages transmitted for a skill search. The MWAC (Multi-Wireless-Agent Communication) model is extended to take some specificities of social networks into account, like the information held by the social network members and their connections with the others.
The network of agents may represent a social network. Each agent holds information about members (agents) and is linked with other agents which represent its neighborhood. Each member builds a local network representation according to its needs. A global view of a social network is not dynamically available and furthermore not desirable. Our approach is thus based on a decentralized interrogation of the network and on organizational structures detection.
With this method, a search does not need to flood all the network with the query because it uses the self-organized structure that emerges so, it can avoid a saturation in the network and it reduces the amount of transmitted messages. In order to show the interest of the approach, our proposition is validated by several simulations. The applications of this work are related to skill searching in a virtual social network.