Indexed Metrics for Link Prediction in Graph Analytics

With the explosive growth of the Internet and the desire to harness the value of the information it contains, the prediction of possible links (relationships) between key players in social networks based on graph-theory principles has garnered great attention in recent years. Consequently, many fields of scientific research have converged in the development of graph analysis techniques to examine the structure of social networks with a very large number of users. However, the relationship between persons within the social network may not be evident when the data-capture process is incomplete or a relationship may have not yet developed between participants who will establish some form of actual interaction in the future. As such, the link-prediction metrics for certain social networks such as criminal networks, which tend to have highly inaccurate data records, may need to incorporate additional circumstantial factors (metadata) to improve their predictive accuracy. One of the key difficulties in link-prediction methods is extracting the structural attributes necessary for the classification of links. In this research, we analysed a few key structural attributes of a network-oriented dataset based on proposed social network analysis (SNA) metrics for the development of link-prediction models. By combining structural features and metadata, the objective of this research was to develop a prediction model that leverages the deep reinforcement learning (DRL) classification technique to predict links/edges even on relatively small-scale datasets, which can constrain the ability to train supervised machine-learning models that have adequate predictive accuracy.

@article{Lim2020,
  title     = {Indexed Metrics for Link Prediction in Graph Analytics},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {5},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Marcus Lim and Azween Abdullah and NZ Jhanjhi and Mahadevan Supramaniam},
  doi       = {10.14569/IJACSA.2020.0110525},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110525}
}