A Scalable Proximity Measure for Link Prediction via Low-rank Matrix Estimation
- 10.2991/csss-14.2014.1How to use a DOI?
- link prediction; social network; proximity measure; low-rank estimation; data mining
Recent years, the link prediction problem in social network and other complex networks become a popular research field. One of the most significant task in link prediction is to design the proximity measure to calculate the similarities of the nodes in the network. The potential structure of the networks in the link prediction problem can be learned from the network data. In this paper, we propose a data-dependent proximity measure under the low-rank assumption in the social network and many other complex networks, then design a scalable matrix estimation algorithm to figure out the proximity measure. According to our experiment results, the proposed proximity measure can get competitive performance compared with other state-of-the-art methods and can be scalable for complex network link prediction.
- © 2014, 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 - Liu Ye AU - Wang Zhisheng AU - Yin Jian AU - Pan Yan PY - 2014/06 DA - 2014/06 TI - A Scalable Proximity Measure for Link Prediction via Low-rank Matrix Estimation BT - Proceedings of the 3rd International Conference on Computer Science and Service System PB - Atlantis Press SP - 1 EP - 4 SN - 1951-6851 UR - https://doi.org/10.2991/csss-14.2014.1 DO - 10.2991/csss-14.2014.1 ID - Ye2014/06 ER -