Revamp social neural network application of Reality Mining

Abstract

Assembling and scrutinizing of on-going communicational-follow in general public enables us to figure behavioral structure of its individuals. Reality-Mining the center idea to bolster this empowers us to gather advanced breadcrumbs left by individuals while they perform their daily routine. Gathering of these signs through sociometric identifications and afterward detailing them for a visual perspective is indicated in the further segment of this paper. The model proposed in this paper is taking into account multi-level information gathering and filtration framework. In this model society is divided in groups on the premise of their intra-group and inter-group interactions. It determines the sequestered group and the speediest data disseminating group. This filtration is handled on the server and all the information exchanges are refined with secure protocols. For gathering of communicational traces we argue utilization of cell phones as sensors, which process information to further server. Further inclusion of influential model and centrality methodologies empower us to recognize most compelling individual in the sub-group. Usage of web based multi-level architecture permits simple expansion, more extensive territory scope, storing and handling huge log records and simple integration with pre-existing communication network.