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.
N. Li and S. K. Das, "Local View based Connectivity Search in Online Social Networks," INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019, pp. 372-377, Apr 2019.
The definitive version is available at https://doi.org/10.1109/INFCOMW.2019.8845271
INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
Center for High Performance Computing Research
Keywords and Phrases
local view; minimum steiner tree; online social networks; search; subgraph connectivity
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Apr 2019