A Similarity Link Prediction Method in Complex Network Based on Endpoint Clustering

DOI

10.2991/amee-17.2017.54How to use a DOI?

Keywords

complex network; link prediction; agglomeration, similarity

Abstract

Link prediction aims to predict the probability of the existence of links between two endpoints in complex network. Many methods ignore the clustering of endpoints when calculate the similarity between two endpoints. To distinguish the contribution of endpoints clustering, we propose a similarity link prediction method based on endpoint clustering. In order to improve the link prediction accuracy, the method considers both the common neighbor and endpoint clustering. Empirical study on six real networks has shown that the method we proposed can achieve a good performance, compared with CN, AA, RA, LP and Katz.

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/).

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TY  - CONF
AU  - Yang Yang
AU  - Yuchun Xu
AU  - Xin Yang
PY  - 2017/09
DA  - 2017/09
TI  - A Similarity Link Prediction Method in Complex Network Based on Endpoint Clustering
BT  - Proceedings of the 2017 2nd International Conference on Automation, Mechanical and Electrical Engineering (AMEE 2017)
PB  - Atlantis Press
SP  - 263
EP  - 265
SN  - 2352-5401
UR  - https://doi.org/10.2991/amee-17.2017.54
DO  - 10.2991/amee-17.2017.54
ID  - Yang2017/09
ER  -