Musial-Gabrys, K. and Gao, F., 2016. Hybrid Link Prediction Model. In: The Sixth Workshop on Social Network Analysis in Applications (SNAA 2016) co-located with International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016), 18-21 August 2016, San Francisco, USA.
Full text available as:
|
PDF
SNAA2016_OA.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. 2MB | |
Copyright to original material in this document is with the original owner(s). Access to this content through BURO is granted on condition that you use it only for research, scholarly or other non-commercial purposes. If you wish to use it for any other purposes, you must contact BU via BURO@bournemouth.ac.uk. Any third party copyright material in this document remains the property of its respective owner(s). BU grants no licence for further use of that third party material. |
Abstract
In network science several topology--based link prediction methods have been developed so far. The classic social network link prediction approach takes as an input a snapshot of a whole network. However, with human activities behind it, this social network keeps changing. In this paper, we consider link prediction problem as a time--series problem and propose a hybrid link prediction model that combines eight structure-based prediction methods and self-adapts the weights assigned to each included method. To test the model, we perform experiments on two real world networks with both sliding and growing window scenarios. The results show that our model outperforms other structure--based methods when both precision and recall of the prediction results are considered.
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Group: | Faculty of Science & Technology |
ID Code: | 24645 |
Deposited By: | Symplectic RT2 |
Deposited On: | 07 Sep 2016 13:07 |
Last Modified: | 14 Mar 2022 13:58 |
Downloads
Downloads per month over past year
Repository Staff Only - |