Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)

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
https://doi.org/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/).

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Volume Title
Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)
Series
Advances in Engineering Research
Publication Date
April 2017
ISBN
978-94-6252-331-9
ISSN
2352-5401
DOI
https://doi.org/10.2991/fmsmt-17.2017.96How to use a DOI?
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  - https://doi.org/10.2991/fmsmt-17.2017.96
ID  - Li2017/04
ER  -