Evaluating link prediction methods

Yang, Yang; Lichtenwalter, Ryan N.; Chawla, Nitesh V.

doi:10.1007/s10115-014-0789-0

Cited by 199 publications

(183 citation statements)

References 52 publications

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“…AUC (area under the receiver operating characteristic curve) and precision are the two widely used evaluation metrics for link prediction8. However, recent works2526 clearly demonstrate that AUC is a deceptive measure for the evaluation of link prediction. The reasons are as follows: firstly, AUC needs the definition of a negative set, which is composed by all the missing (unobserved) links in the network except for the removed links (for test) that compose the positive set.…”

Section: Resultsmentioning

confidence: 99%

A perturbation-based framework for link prediction via non-negative matrix factorization

Wang

Cai

Jiao

et al. 2016

Sci Rep

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Many link prediction methods have been developed to infer unobserved links or predict latent links based on the observed network structure. However, due to network noises and irregular links in real network, the performances of existed methods are usually limited. Considering random noises and irregular links, we propose a perturbation-based framework based on Non-negative Matrix Factorization to predict missing links. We first automatically determine the suitable number of latent features, which is inner rank in NMF, by Colibri method. Then, we perturb training set of a network by perturbation sets many times and get a series of perturbed networks. Finally, the common basis matrix and coefficients matrix of these perturbed networks are obtained via NMF and form similarity matrix of the network for link prediction. Experimental results on fifteen real networks show that the proposed framework has competitive performances compared with state-of-the-art link prediction methods. Correlations between the performances of different methods and the statistics of networks show that those methods with good precisions have similar consistence.

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Section: Resultsmentioning

confidence: 99%

A perturbation-based framework for link prediction via non-negative matrix factorization

Wang

Cai

Jiao

et al. 2016

Sci Rep

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“…We evaluate the ranking results by using the area under the Receiver Operating Characteristic curve (AUROC), the area under the Precision-Recall curve (AUPR), and precision at top k%, where we change k from 1 to 10 with 1 as the interval and from 10 to 100 with 10 as the interval in our experiments. AU-ROC, AUPR and precision at top ranked links are typically used in the evaluation of link prediction tasks [46,28,35,23,42,3,44].…”

Section: Methodsmentioning

confidence: 99%

CoupledLP

Dong

Zhang

Tang

et al. 2015

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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We study the problem of link prediction in coupled networks, where we have the structure information of one (source) network and the interactions between this network and another (target) network. The goal is to predict the missing links in the target network. The problem is extremely challenging as we do not have any information of the target network. Moreover, the source and target networks are usually heterogeneous and have different types of nodes and links. How to utilize the structure information in the source network for predicting links in the target network? How to leverage the heterogeneous interactions between the two networks for the prediction task?We propose a unified framework, CoupledLP, to solve the problem. Given two coupled networks, we first leverage atomic propagation rules to automatically construct implicit links in the target network for addressing the challenge of target network incompleteness, and then propose a Coupled Factor Graph Model to incorporate the meta-paths extracted from the coupled part of the two networks for transferring heterogeneous knowledge. We evaluate the proposed framework on two different genres of datasets: diseasegene (DG) and mobile social networks. In the DG networks, we aim to use the disease network to predict the associations between genes. In the mobile networks, we aim to use the mobile communication network of one mobile operator to infer the network structure of its competitors. On both datasets, the proposed CoupledLP framework outperforms several alternative methods. The proposed problem of coupled link prediction and the corresponding framework demonstrate both the scientific and business applications in biology and social networks.

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“…The AUC (area under the receiver operating characteristic curve) is found by the probability that a randomly chosen missing link (i.e. positive pairs in test set) is given a higher score than a randomly chosen non-existent link32. However, this measure has been found as a deceptive evaluation measure3233, epecially in imbalanced data problems.…”

Section: Methodsmentioning

confidence: 99%

Mutual information model for link prediction in heterogeneous complex networks

Shakibian

Charkari

2017

Sci Rep

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Recently, a number of meta-path based similarity indices like PathSim, HeteSim, and random walk have been proposed for link prediction in heterogeneous complex networks. However, these indices suffer from two major drawbacks. Firstly, they are primarily dependent on the connectivity degrees of node pairs without considering the further information provided by the given meta-path. Secondly, most of them are required to use a single and usually symmetric meta-path in advance. Hence, employing a set of different meta-paths is not straightforward. To tackle with these problems, we propose a mutual information model for link prediction in heterogeneous complex networks. The proposed model, called as Meta-path based Mutual Information Index (MMI), introduces meta-path based link entropy to estimate the link likelihood and could be carried on a set of available meta-paths. This estimation measures the amount of information through the paths instead of measuring the amount of connectivity between the node pairs. The experimental results on a Bibliography network show that the MMI obtains high prediction accuracy compared with other popular similarity indices.

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Evaluating link prediction methods

Cited by 199 publications

References 52 publications

A perturbation-based framework for link prediction via non-negative matrix factorization

A perturbation-based framework for link prediction via non-negative matrix factorization

CoupledLP

Mutual information model for link prediction in heterogeneous complex networks

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