Relational Attribute Integrated Matching Analysis (RAIMA): A Framework for the Design of Self-Adaptive Egocentric Social Networks

An emerging research in pervasive computing is the inference of social context to facilitate and mediate communications among proximate people. Understanding users´ needs through information reasoning and leveraging principles of social networks play an important role in the emergence of innovative computer-mediated social networks. This paper introduces a generic social networking framework for the analysis and visualization of mobile and spontaneous social networks. The proposed framework is capable of analyzing social scores in order to provide decision support to users in the form of egocentric social graphs. As part of the framework, we introduce a matching algorithm that its efficiency is compared to commonly used “Stable Marriage Matching” algorithms in opportunistic social networks. We show the performance of the algorithm as social profile attributes increase in a network.