Open AccessA novel similarity measure for missing link prediction in social networks
Author(s) -
G. Naga Chandrika,
E. Sreenivasa Reddy
Publication year - 2020
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v19.i2.pp1071-1077
Subject(s) - similarity (geometry) , link (geometry) , measure (data warehouse) , node (physics) , variety (cybernetics) , computer science , social network (sociolinguistics) , complex network , data mining , social network analysis , artificial intelligence , machine learning , computer network , social media , engineering , world wide web , structural engineering , image (mathematics)
Social Networks progress over time by the addition of new nodes and links, form associations with one community to the other community. Over a few decades, the fast expansion of Social Networks has attracted many researchers to pay more attention towards complex networks, the collection of social data, understand the social behaviors of complex networks and predict future conflicts. Thus, Link prediction is imperative to do research with social networks and network theory. The objective of this research is to find the hidden patterns and uncovered missing links over complex networks. Here, we developed a new similarity measure to predict missing links over social networks. The new method is computed on common neighbors with node-to-node distance to get better accuracy of missing link prediction. We tested the proposed measure on a variety of real-world linked datasets which are formed from various linked social networks. The proposed approach performance is compared with contemporary link prediction methods. Our measure makes very effective and intuitive in predicting disappeared links in linked social networks.