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sonLP: social network link prediction by principal component regression


Bao, Z and Zeng, Y and Tay, TC, sonLP: social network link prediction by principal component regression, Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 25-28 August 2013, Niagara Falls, Canada, pp. 364-371. ISBN 978-1-4503-2240-9 (2014) [Refereed Conference Paper]

Copyright Statement

Copyright 2012 IEEE

DOI: doi:10.1145/2492517.2492558



Social networks are driven by social interaction and therefore dynamic. When modeled as a graph, nodes and links are continually added and deleted, and there is considerable interest in social network analysis on predicting link formation. Current work has not adequately addressed three issues:


(1) Most link predictors start with using features from the link topology as input. How do features in other dimensions of the social network data affect link formation? (2) The dynamic nature of social networks implies the features driving link formation are constantly changing. How can a predictor automatically select the features that are important for link formation? (3) Node pairs that are not linked can outnumber links by orders of magnitude, but previous work do not address this imbalance. How can we design a predictor that is robust with respect to link imbalance?


This paper presents sonLP, a social network link predictor. It uses principal component analysis to identify features that are important to link prediction, its tradeoff between true and false positives is near optimal for a wide range of link imbalance, and it has optimal time complexity. Experiments with coauthorship prediction in the ACM researcher community also show the importance of using features outside the links’ dimension.

Item Details

Item Type:Refereed Conference Paper
Keywords:social network analysis, link prediction, data mining, imbalanced samples
Research Division:Information and Computing Sciences
Research Group:Data management and data science
Research Field:Data management and data science not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Information services
Objective Field:Electronic information storage and retrieval services
UTAS Author:Bao, Z (Dr Zhifeng Bao)
ID Code:92174
Year Published:2014 (online first 2013)
Deposited By:Information and Communication Technology
Deposited On:2014-06-09
Last Modified:2015-02-05

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