Link Prediction via Extended Resource Allocation Index

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/).

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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  -