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An ensemble approach to link prediction

An ensemble approach to link prediction
An ensemble approach to link prediction
A network with n nodes contains O(n 2 ) possible links. Even for networks of modest size, it is often difficult to evaluate all pairwise possibilities for links in a meaningful way. Further, even though link prediction is closely related to missing value estimation problems, it is often difficult to use sophisticated models such as latent factor methods because of their computational complexity on large networks. Hence, most known link prediction methods are designed for evaluating the link propensity on a specified subset of links, rather than on the entire networks. In practice, however, it is essential to perform an exhaustive search over the entire networks. In this article, we propose an ensemble enabled approach to scaling up link prediction, by decomposing traditional link prediction problems into subproblems of smaller size. These subproblems are each solved with latent factor models, which can be effectively implemented on networks of modest size. By incorporating with the characteristics of link prediction, the ensemble approach further reduces the sizes of subproblems without sacrificing its prediction accuracy. The ensemble enabled approach has several advantages in terms of performance, and our experimental results demonstrate the effectiveness and scalability of our approach.
1041-4347
2402-2416
Duan, Liang
96dc72bd-df11-4426-a122-71a14b0ff82b
Ma, Shuai
2e1bce38-2f4a-410a-862e-752dc885439b
Aggarwal, Charu
872194c8-2292-4e37-9c74-f8a9dd2f75a5
Ma, Tiejun
1f591849-f17c-4209-9f42-e6587b499bae
Huai, Jinpeng
0f9c7a01-06f1-4549-95d5-8741127653ca
Duan, Liang
96dc72bd-df11-4426-a122-71a14b0ff82b
Ma, Shuai
2e1bce38-2f4a-410a-862e-752dc885439b
Aggarwal, Charu
872194c8-2292-4e37-9c74-f8a9dd2f75a5
Ma, Tiejun
1f591849-f17c-4209-9f42-e6587b499bae
Huai, Jinpeng
0f9c7a01-06f1-4549-95d5-8741127653ca

Duan, Liang, Ma, Shuai, Aggarwal, Charu, Ma, Tiejun and Huai, Jinpeng (2017) An ensemble approach to link prediction. IEEE Transactions on Knowledge and Data Engineering, 29 (11), 2402-2416. (doi:10.1109/TKDE.2017.2730207).

Record type: Article

Abstract

A network with n nodes contains O(n 2 ) possible links. Even for networks of modest size, it is often difficult to evaluate all pairwise possibilities for links in a meaningful way. Further, even though link prediction is closely related to missing value estimation problems, it is often difficult to use sophisticated models such as latent factor methods because of their computational complexity on large networks. Hence, most known link prediction methods are designed for evaluating the link propensity on a specified subset of links, rather than on the entire networks. In practice, however, it is essential to perform an exhaustive search over the entire networks. In this article, we propose an ensemble enabled approach to scaling up link prediction, by decomposing traditional link prediction problems into subproblems of smaller size. These subproblems are each solved with latent factor models, which can be effectively implemented on networks of modest size. By incorporating with the characteristics of link prediction, the ensemble approach further reduces the sizes of subproblems without sacrificing its prediction accuracy. The ensemble enabled approach has several advantages in terms of performance, and our experimental results demonstrate the effectiveness and scalability of our approach.

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More information

Accepted/In Press date: 5 July 2017
e-pub ahead of print date: 21 July 2017
Published date: November 2017

Identifiers

Local EPrints ID: 415281
URI: https://eprints.soton.ac.uk/id/eprint/415281
ISSN: 1041-4347
PURE UUID: e99ed220-7ebb-4b37-98b2-47e844b6bfac

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Date deposited: 06 Nov 2017 17:30
Last modified: 13 Jun 2019 16:30

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