A Survey on Semantic Web and Big Data Technologies for Social Network Analysis

Abstract

Social Network Analysis (SNA) has become a very important and increasingly popular topic among researchers in recent years especially after emerging Semantic Web and Big Data technologies. Social networking services such as Facebook, Google+, Twitter, etc. provide large amounts of data that can be used for social network analysis by researchers. Semantic Web technology plays an important role for collecting, merging, and aggregating social network data from heterogeneous sources more easily, robustly and in an interoperable manner. Today, data scientists use several different frameworks for querying, integrating and analyzing datasets located at different sources. Meanwhile, most of the big social data is in unstructured or semi-structured format. Big data architectures allow researchers to analyze unstructured data in a time and cost-efficient way. New approaches for SNA are needed to combine Semantic Web and Big Data technologies in order to utilize and add capabilities to existing solutions. To be able to analyze large scale social networks, algorithms should have scalable designs in order to benefit from the emerging Big Data technologies. This survey focuses on recently developed systems for SNA and summarizes the state-of-the-art technologies used by them and points out to future research directions.