We are living in the age of Higher Education (HE) 2.0, where the fastest growing social networking sites (SNS) is Facebook. It's been widely accepted as a platform for communications and collaborations. The students and Higher Education Professionals (HEP) which includes faculty members, administrators, management, etc. use to share their valuable resources. It's been notified that students are far more ahead in using communication technologies as compared to the Higher Education Institutions (HEIs) Professionals. In our research work, we analyse the social network graphs which are Facebook pages or groups having members like HEP, students, etc., related to Higher Education in India. We discovered that in some networks, members are less, but online posts and messages are huge in number. Student's active participations or interactions in the social networks can be done by Social Network Analysis (SNA) approaches. In our study, we explore the techniques for the SNA to detect the motifs or patterns in the Online Social Media Networks (OSMNs). These are produced by the interactions and the content exchanged among the users, students and professionals of HE in the social graph. In our previous research work, we used Network Analysis Software Applications (NASA) tools able to discover important architecture and fetching the core, perimetric and outlined members in the network. By these NASA applications, we can visualize, generate and summarize the buzz topics or the words by the active members of the network, which gives us a glimpse of the topic under discussion. We exploited the mining algorithms provided by the NASA tools like (Gephi, NodeXL, LikeAnalyzer and Wolfram Alpha). This helped us in assessing their presence and participation factor of the students and the higher education professionals in the social network graphs. Our study attempted to test for an affiliation that exists between Higher Education (HE) and the Social Networking Site (SNS) Facebook. Our research in analysis and finding related to social network analysis predicts that social networking on Facebook and higher education can work in parallel.
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