Link Prediction via Extended Resource Allocation Index
Authors
Longjie Li, Shenshen Bai, Shiyu Yang, Longyu Qu, Yiwei Yang
Corresponding Author
Longjie Li
Available Online April 2017.
- DOI
- 10.2991/fmsmt-17.2017.96How to use a DOI?
- Keywords
- complex network, link prediction, resource allocation, quasi-local index
- Abstract
Link prediction is an important branch of complex network analysis, which can identify the missing or future links in a network. In this paper, a new link prediction method is presented, inspired by the ideas of both resource allocation index and quasi-local indices, to estimate the likelihood of existing a link between two unconnected nodes. To evaluate the prediction accuracy of the new index, we conduct experiments on five real-world networks compared with five famous indices. The results show that our new index outperforms the five baselines on the five networks.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Longjie Li AU - Shenshen Bai AU - Shiyu Yang AU - Longyu Qu AU - Yiwei Yang PY - 2017/04 DA - 2017/04 TI - Link Prediction via Extended Resource Allocation Index BT - Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017) PB - Atlantis Press SP - 455 EP - 460 SN - 2352-5401 UR - https://doi.org/10.2991/fmsmt-17.2017.96 DO - 10.2991/fmsmt-17.2017.96 ID - Li2017/04 ER -