TY - GEN
T1 - Link prediction by correlation on social network
AU - Rahman, Md Shafiur
AU - Dey, Leema Rani
AU - Haider, Sajal
AU - Uddin, Md Ashraf
AU - Islam, Manowarul
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - In a social network, the topology of the network grows through the formation of the link. the connection between two nodes in a social network indicates a confidence in terms of the similarity of some activities. Generally, a new link in the social network is created from different perspectives such as familiarity, cohesiveness, geographical locations etc. The concept of the link in the social network has been utilized to discover the hidden meaning of different fields such as e-commerce, bioinformatics and information retrieval. The prediction of a new link between two nodes in the social network is normally accomplished based on the nature of the topology and the similarity function among the nodes is defined with the help of the number of common friends. In this paper, we propose two link prediction algorithms: Local Link Prediction Algorithm and Global Link prediction by taking into consideration of user's activities as well as the common friends. We apply two formulas called correlation based cScore and influential score based iScore to measure the similarity between the two predicted nodes. Finally, we analyze the performance of the proposed algorithms by using DBLP, PPI, PB, and USAir data sets and the experimental result attests that our link predicted algorithm outperforms over the existing algorithms.
AB - In a social network, the topology of the network grows through the formation of the link. the connection between two nodes in a social network indicates a confidence in terms of the similarity of some activities. Generally, a new link in the social network is created from different perspectives such as familiarity, cohesiveness, geographical locations etc. The concept of the link in the social network has been utilized to discover the hidden meaning of different fields such as e-commerce, bioinformatics and information retrieval. The prediction of a new link between two nodes in the social network is normally accomplished based on the nature of the topology and the similarity function among the nodes is defined with the help of the number of common friends. In this paper, we propose two link prediction algorithms: Local Link Prediction Algorithm and Global Link prediction by taking into consideration of user's activities as well as the common friends. We apply two formulas called correlation based cScore and influential score based iScore to measure the similarity between the two predicted nodes. Finally, we analyze the performance of the proposed algorithms by using DBLP, PPI, PB, and USAir data sets and the experimental result attests that our link predicted algorithm outperforms over the existing algorithms.
KW - Correlation
KW - GPLA
KW - Global Link Prediction
KW - Influential Score
KW - LLPA
KW - Link Prediction
KW - Local Link Prediction
KW - Node Activities
KW - Social Network
UR - http://www.scopus.com/inward/record.url?scp=85050386722&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050386722&partnerID=8YFLogxK
U2 - 10.1109/ICCITECHN.2017.8281812
DO - 10.1109/ICCITECHN.2017.8281812
M3 - Conference contribution
AN - SCOPUS:85050386722
T3 - 20th International Conference of Computer and Information Technology, ICCIT 2017
SP - 1
EP - 6
BT - 20th International Conference of Computer and Information Technology, ICCIT 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th International Conference of Computer and Information Technology, ICCIT 2017
Y2 - 22 December 2017 through 24 December 2017
ER -