Abstract: Currently, social networks have brought about an enormous number of users connecting to such systems over a couple of years, whereas the link mining is a key research track in this area. It has pulled the consideration of several analysts as a powerful system to be utilized as a part of social networks study to understand the relations between nodes in social circles. Numerous data sets of today’s interest are most appropriately called as a collection of interrelated linked objects. The main challenge faced by analysts is to tackle the problem of structured data sets among the objects. For this purpose, we design a new comprehensive model that involves link mining techniques with semantics to perform link mining on structured data sets. The past work, to our knowledge, has investigated on these structured datasets using this technique. For this purpose, we extracted real-time data of posts using different tools from one of the famous SN platforms and check the society’s behavior against it. We have verified our model utilizing diverse classifiers and the derived outcomes inspiring.

Maria Ijaz, Javed Ferzund, Muhammad Asif Suryani and Anam Sardar, “Social Network Link Prediction using Semantics Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 9(1), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090138

@article{Ijaz2018,
title = {Social Network Link Prediction using Semantics Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090138},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090138},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {1},
author = {Maria Ijaz and Javed Ferzund and Muhammad Asif Suryani and Anam Sardar}
}