The full text of this article
Prediction of missing links in social networks: feature integration with node neighbour
by Anand Kumar Gupta; Neetu Sardana
International Journal of Web Based Communities (IJWBC), Vol. 14, No. 1, 2018
Abstract: Link prediction techniques are used to identify the future network structure on the basis of existing connectivity pattern of the users. Most of the existing link prediction techniques employ varied similarity indices to predict new links in network. Some techniques use common neighbours while others use common shared profile information of the user for prediction. Typically existing link prediction techniques have only focused on one of these two data modalities: common neighbours or common attributes. Both of them play equally important role in the dynamics of the network. In this paper, we propose a feature integrated node neighbour (FINN) approach, an accurate algorithm for predicting links in network. FINN integrates Jaccard coefficient and Adamic Adar to predict link between nodes using their connections and features. We have evaluated FINN by implementing it over the real-time Facebook dataset collected from SNAP repository and validated the result through area under ROC curve.
Online publication date: Fri, 16-Mar-2018
is only available to individual subscribers or to users at subscribing institutions.
Go to Inderscience Online Journals to access the Full Text of this article.
Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.
Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Web Based Communities (IJWBC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable).
See our Orders page to subscribe.
If you still need assistance, please email email@example.com