Title: Big data analytics for exploratory social network analysis

Authors: Chetna Dabas

Addresses: Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India

Abstract: If an organisation desires to retrieve productive insights, big data analytics plays a vital role in analysing the unstructured, semi-structured and structured data. Big data assumes human-sourced information (social network analysis), machine-generated data and process-mediated data. Big data as a product of social networks comes from human experiences in works of art or in books, video, photographs, etc. A small piece of information that might have begun with a suggestion of purchasing a smart phone during group chats amongst a circle of friends might end up on the desk of a smart phone company manager as an aid to decision making. This paper aims to address big data analytics for exploratory social network and proposes an experimental study with results. Experimentation has been carried out on SocNetV Version 1.9 using Pajek and different metrics of SNA are evaluated and analysed to strengthen decision making.

Keywords: big data analytics; data visualisation; social network analysis; SNA.

DOI: 10.1504/IJITM.2017.086864

International Journal of Information Technology and Management, 2017 Vol.16 No.4, pp.348 - 359

Received: 25 Sep 2015
Accepted: 01 Feb 2016

Published online: 19 Jun 2017 *

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