Mining Individual Features to Enhance Link Prediction Efficiency in Location Based Social Networks

Date

2018-08-31

Author

Bayrak, Ahmet Engin
Polat, Faruk

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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One of the most attractive problems of social network analysis is the link prediction. Social networks' user growth is mostly supported with data driven friend recommendations which are provided by link predictors. Previously, we had studied new features to improve prediction accuracy in Location Based Social Networks (LBSNs) where users share temporal location information with check-in interactions. In this paper, we focused on the efficiency of link predictors as the speed of prediction is as critical as its accuracy in LBSNs. Extraction time costs and prediction accuracy of individual LBSN features are mined to pick a feature subset that is achieving faster link prediction while not losing from accuracy.

Subject Keywords

Social network services, Feature extraction, Entropy, Measurement , Electronic mail , Network topology , Topology

URI

https://hdl.handle.net/11511/55928

Conference Name

IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

A. E. Bayrak and F. Polat, “Mining Individual Features to Enhance Link Prediction Efficiency in Location Based Social Networks,” presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, SPAIN, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55928.