A Systematic Analysis of Link Prediction in Complex Network - Haji Gul; Adnan Amin; Awais Adnan; Kaizhu Huang (IEEE Access, 2021)
Link mining is an important task in the field of data mining and has numerous applications in informal community. Suppose a real-world complex network, the responsibility of this function is to anticipate those links which are not occurred yet in the given real-world network. Holding the significance of LP, the link mining or expectation job has gotten generous consideration from scientists in differing exercise. In this manner, countless strategies for taking care of this issue have been proposed in the late decades. Various articles of link prediction are accessible, however, these are antiquated as multiples new methodologies introduced. In this paper, give a precise assessment of prevail link mining approaches. The investigation is through, it consists the soonest scoring-based approaches and reaches out to the latest strategies which confide on different link prediction strategies. We additionally order link prediction strategies because of their specialized methodology and discussion about the quality and weaknesses of various techniques. Additionally, we compared and expounded various top link prediction techniques. The experimental results of these techniques, over twelve data-sets are ordered here based on performance, RA, 0.7411 > AA, 0.7285 > PA, 0.7202 > Katz, 0.7141 > CN, 0.6951 > HP, 0.6924 > LHN, 0.6017 > PD, 0.3978.