Perceptual computing in social networks

We describe a computationally feasible approach to perceptual computing in large social networks, where the attribute memberships and relationship strengths are described using words, and the words are represented by interval type-2 fuzzy membership functions. By employing pre-computation and storage, we reduce the queries of such networks to simple arithmetic calculations that can be feasibly performed on-the-fly.