TY - JOUR
T1 - A Systematic Analysis of Link Prediction in Complex Network
AU - Gul, Haji
AU - Amin, Adnan
AU - Adnan, Awais
AU - Huang, Kaizhu
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61876155, in part by the Jiangsu Science and Technology Programme under Grant BE2020006-4 and Grant BK20181189, and in part by the Key Program Special Fund in XJTLU under Grant KSF-T-06.
Publisher Copyright:
© 2013 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Complex Networks
KW - Complex networks
KW - Data mining
KW - Licenses
KW - Link Prediction
KW - Prediction and Recommendation
KW - Social networking (online)
KW - Support vector machines
KW - Systematics
KW - Task analysis
UR - http://www.scopus.com/inward/record.url?scp=85106785759&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3053995
DO - 10.1109/ACCESS.2021.3053995
M3 - Article
AN - SCOPUS:85106785759
VL - 9
SP - 20531
EP - 20541
JO - IEEE Access
JF - IEEE Access
M1 - 9334982
ER -