Local View Based Connectivity Search in Online Social Networks
One of the challenges in social media research is that, often times, researchers or third parties could not obtain the massive of data collected by a limited number of 'big brothers' (e.g., Facebook and Google). In this paper, we shed light on leveraging social network topological properties and local information to effectively conduct search in Online Social Networks (OSN). The problem we focus on is to discover the reachability of a group of target people in an OSN, particularly from the perspective of a third-party analyst who does not have full access to the OSN. We developed effective and efficient detection techniques which demand only a small number of queries to discover people's connections (e.g. friendship) in the OSN. After conducting experiments on real-world data sets, we found that our proposed techniques perform as well as the centralized detection algorithm, which assumes the availability of the global information in the OSN.
Li, N., & Das, S. (2019). Local View Based Connectivity Search in Online Social Networks. Retrieved from https://digitalcommons.pvamu.edu/computer-information-facpubs/42