Supervised link prediction in weighted networks

de, Hially Rodrigues; Prudêncio, Ricardo B. C.

doi:10.1109/ijcnn.2011.6033513

Cited by 83 publications

(40 citation statements)

References 20 publications

(45 reference statements)

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“…The same features are used by all methods, with the only difference being the weights on the network links. In this article, we compare the proposed method LPSF with alternate weighting schemes, such as the number of co-authored papers, as suggested in [22]. We see that in both DBLP datasets, Unweighted, Weighted and LPSF perform almost equally under Precision, though LPSF performs somewhat worse for some classifiers (Random Forest and Naive Bayes).…”

Section: Varying the Number Of Social Featuresmentioning

confidence: 92%

“…De Sá and Prudêncio investigated the use of weights to improve the performance of supervised link prediction [22]. In their work, they extend eight benchmark unsupervised metrics for weighted networks, and adopt prediction scores as node pairs' attributes for a supervised classification model.…”

Section: Related Workmentioning

confidence: 99%

“…However, proximities between nodes can be estimated better by using both graph proximity measures and the weights of existing links [22,23]. Much of this prior work uses the number of encounters between users as the link weights.…”

Section: Proposed Lpsf Framework: Reweighting the Network + Supervisementioning

confidence: 99%

“…Therefore Logistic Regression and Neural Network are a better choice for LPSF in that they improve the Recall performance without decreasing the Precision. Using the traditional weighted features [22] does not help supervised classifiers for link prediction to a great extent. As discussed above, reweighting the unweighted collaboration network using our proposed technique, LPSF, performs the best.…”

Section: Varying the Number Of Social Featuresmentioning

confidence: 99%

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Link Prediction in Heterogeneous Collaboration Networks

Wang

Sukthankar

2014

Lecture Notes in Social Networks

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Abstract. Traditional link prediction techniques primarily focus on the effect of potential linkages on the local network neighborhood or the paths between nodes. In this article, we study both supervised and unsupervised link prediction in networks where instances can simultaneously belong to multiple communities, engendering different types of collaborations. Links in these networks arise from heterogeneous causes, limiting the performance of predictors that treat all links homogeneously. To solve this problem, we introduce a new supervised link prediction framework, Link Prediction using Social Features (LPSF ), which incorporates a reweighting scheme for the network based on nodes' features extracted from patterns of prominent interactions across the network.Experiments on coauthorship networks demonstrate that the choice for measuring link weights can be critical for the link prediction task. Our proposed reweighting method in LPSF better expresses the intrinsic relationship between nodes and improves prediction accuracy for supervised link prediction techniques. We also compare the unsupervised performance of the individual features used within LPSF with two new diffusion-based methods: LPDP (Link Prediction using Diffusion Process) and LPDM (Link Prediction using Diffusion Maps). Experiments demonstrate that LPDP is able to identify similar node pairs, even far away ones, that are connected by weak ties in the coauthorship network using the diffusion process; however, reweighting the network has little impact on prediction performance.

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Section: Varying the Number Of Social Featuresmentioning

confidence: 92%

Section: Related Workmentioning

confidence: 99%

Section: Proposed Lpsf Framework: Reweighting the Network + Supervisementioning

confidence: 99%

Section: Varying the Number Of Social Featuresmentioning

confidence: 99%

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Link Prediction in Heterogeneous Collaboration Networks

Wang

Sukthankar

2014

Lecture Notes in Social Networks

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“…As previously mentioned, the starting point of this approach is to extract the values/scores of different measures that indicate the similarity between pairs of nodes. These scores can be used either by unsupervised (Liben-Nowell et al, 2003;Lu & Zhou, 2011;Murata & Moriyasu, 2008) or supervised link prediction (Hasan et al, 2006;Lichtenwalter, Lussier, & Chawla, 2010;Sá & Prudêncio, 2011). In the former approach, a proximity measure is chosen and deployed to rank node pairs in the network.…”

Section: Link Predictionmentioning

confidence: 99%

Proximity measures for link prediction based on temporal events

Soares

Prudêncio

2013

Expert Systems with Applications

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Dynamic human contact prediction based on naive Bayes algorithm in mobile social networks

Zeng

Yao

et al. 2019

Softw Pract Exp

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Human contact prediction is a challenging task in mobile social networks. The existing prediction methods are based on the static network structure, and directly applying these static prediction methods to dynamic network prediction is bound to reduce the prediction accuracy. In this paper, we extract some important features to predict human contacts and propose a novel human contact prediction method based on naive Bayes algorithm, which is suitable for dynamic networks. The proposed method takes the ever-changing structure of mobile social networks into account. First, the past time is partitioned into many periods with equal intervals, and each period has a feature matrix of all node pairs. Then, with the feature matrixes used for classifiers training based on naive Bayes algorithm, we can get a classifier for each time period. At last, the different weights are assigned to the classifiers according to their importance to contact prediction, and all classifiers are weighted combination into the final prediction classifier. The extensive experiments are conducted to verify the effectiveness and superiority of the proposed method, and the results show that the proposed method can improve the prediction accuracy and TP Rate to a large extent. Besides, we find that the size of time interval has a certain impact on the clustering coefficient of mobile social networks, which further affects the prediction accuracy.

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Supervised link prediction in weighted networks

Cited by 83 publications

References 20 publications

Link Prediction in Heterogeneous Collaboration Networks

Link Prediction in Heterogeneous Collaboration Networks

Proximity measures for link prediction based on temporal events

Dynamic human contact prediction based on naive Bayes algorithm in mobile social networks

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