A Novel Similarity Measure for Missing Link Prediction in Social Networks

Naga Chandrika Gogulamudi


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 to 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 uncovering missing links over complex networks. Here, we developed a new similarity measure to predict missing links over social networks. The new measure is based 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.


Complex Networks Link Prediction Missing Links Similarity Measure Social Networks

DOI: http://doi.org/10.11591/ijeecs.v19.i2.pp%25p
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