During the last decade social network have become not only popular but also affordable and universally-acclaimed communication means that has thrived in making the world a global village. Social network sites are commonly known for information dissemination, personal activities posting, product reviews, online pictures sharing, professional profiling, advertisements and opinion/sentiment expression. News alerts, breaking news, political debates and government policy are also posted and analysed on social network sites. It is observed that more people are becoming interested in and relying on the social network for information in real time.
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Users sometimes make decisions based on information posted by unfamiliar
individuals on social network [66] increasing the degree of reliance on the credibility of these sites. Social network has succeeded in transforming the way different entities source and retrieve valuable information irrespective of their location. Social network has also given users the privilege to give
opinions with very little or no restriction.
Research Issues on Social Network Analysis
A number of research issues and challenges facing the realisation of utilising data mining techniques in social network analysis could be
identified as follows:
● Linkage-based and Structural Analysis – This is an analysis of
the linkage behaviour of the social network so as to ascertain relevant nodes, links, communities and imminent areas of the network - Aggarwal, 2011.
● Dynamic Analysis and Static Analysis – Static analysis such as in
bibliographic networks is presumed to be easier to carry out than those in streaming networks. In static analysis, it is presumed that social network changes gradually over time and analysis on the entire network can be done in batch mode. Conversely, dynamic analysis of streaming networks like Facebook and YouTube are very difficult to carry out. Data on these networks are generated at
high speed and capacity. Dynamic analysis of these networks are often in the area of interactions between entities - Having presented some of the research issues and challenges in social network analysis, the following sections and sub-sections present the overview of different data mining approaches used in analysing social
network data.
Recommender System in Social Network Community Based on the mutuality between nodes in social network groups, collaborative filtering (CF) technique, which forms one of the three classes of the recommender system (RS), can be used to exploit the association among users [56]. Items can be recommended to a user based on the rating of his mutual connection. Where CF’s main downside is that of data sparsity, content-based (another RS method) explore the structures of the data to produce recommendations. However, the hybrid approaches usually suggest recommendations by combining CF and content-based recommendations. The experiment in [18] proposed a hybrid approach named EntreeC, a system that pools knowledge-based RS and CF to recommend restaurants. The work in [69] improved on CF algorithm by using a greedy implementation of hierarchical agglomerative clustering to suggest forthcoming conferences or journals in which researchers
(especially in computer science) can submit their work.
Semantic Web of Social Network
The Semantic Web platform makes knowledge sharing and re-use possible over different applications and community edges. Discovering the evolvement of Semantic Web (SW) enhances the knowledge of the prominence of Semantic Web Community and envisages the synthesis of the Semantic Web. The work in [92] employed Friend of a Friend (FOAF)
to explore how local and global community level groups develop and evolve in large-scale social networks on the Semantic Web. The study revealed the evolution outlines of social structures and forecasts future drift. Likewise application model of Semantic Web-based Social Network Analysis Model creates the ontological field library of social network analysis combined with the conventional outline of the semantic web to attain
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