Negative sign prediction for signed social networks

Yuan, Weiwei; Li, Chenliang; Han, Guangjie; Guan, Donghai; Zhou, Li; He, Kangya

doi:10.1016/j.future.2017.08.037

Cited by 17 publications

(7 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To further test the performance of prediction of model, it is compared with the existing approaches, such as the logistic regression (LR) proposed by Leskovec et al [4], the logistic regression based on three attributes (LR-3A) proposed by Yuan et al [9], the supervised learning based on higher order cycles (HOC) proposed by Chiang et al [19], the logistic regression based on Bayesian node properties (LR-BNP) proposed by Song et al [23], the troll-trust model based on ranking proposed by Wu et al [24], the logistic regression based on reputation and optimism (LR-RO) proposed by Shahriari et al [26], the measures of imbalance (MOI) and the matrix factorization (MF) studied by Chiang et al [15], the collaborative ltering (CF) introduced by Javari and Jalili [28] and the closed triple micro structure (CTMS) proposed by Khodadadi and Jalili [30]. e comparison results are shown in Table 5.…”

Section: Comparison Of Resultsmentioning

confidence: 99%

“…LR [4] 0.9342 0.9351 0.8021 LR-3A [9] 0.9592 0.8892 0.8786 HOC-5 [19] 0.9080 0.8469 0.8605 LR-BNP [23] 0.9313 0.8565 0.8737 Toll-Trust [24] ≈ 0.96 ≈ 0.90 ≈ 0.89 LR-RO [26] 0.9582 0.9010 0.8880 MOI-10 [15] 0.8497 0.7850 0.8220 MF [15] 0.9448 0.8835 0.8839 CF [30] 0.9282 0.8258 0.8137 CTMS [30] 0 As for the skewness feature of actual datasets, is basically determined by the positive edges. erefore, the of the model is compared with the exiting algorithms, shown in Table 6.…”

Section: Epinions Slashdot Wikipediamentioning

confidence: 99%

“…Positive signs can be propagated between members of social networks while negative signs cannot. For example, A trusts B and B trusts C, A will trust C to some extent, while A distrusts B and B distrusts C, it is hard to judge the relationships between A and C directly [9]. ereby, in the propagation model of reference [10], the distrust relationship only propagates once among the trust relationships.…”

Section: Introductionmentioning

confidence: 99%

“…Agrawal et al [17] and Hsieh et al [18] approximate the original matrix by a matrix decomposition method, in which the original × matrix is decomposed into the product of two × matrices, and the element values of the product matrix are used as the predicted values. To date, the methods used in machine learning include logistic regression [4,9,19,20], support vector machine [21], decision tree [22], naive Bayes [23] etc. ; the features used for learning include nodal degrees [4,9], types [23], similarity [9,20], trustworthiness [24], preference [25,26], triangle structures [4], quadrilateral structures [19], user reviews [22,27] etc.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Sign Prediction on Social Networks Based Nodal Features

Zhu

2020

Complexity

View full text Add to dashboard Cite

The sentiments among social individuals are complexity and diversity, and the relationships between them include being friendly and hostile. The positive (“friendly” ,“like” or “trust”) or negative (“hostile”, “dislike” or “distrust”) sentiments in the relations can be modeled as signed connections or links. The missing relations or sentiments between individuals are always worthy of speculation. The sign predication on links has been significant applications in a variety of online settings, such as online recommendation system and abnormal user detections. A novel sign prediction method called the SPR model is measured by the values of the two indexes, one is similarity; the other is preference-reputation (PR). The similarity of a pair nodes is defined by the statistical properties of local structures. The definition of similarity agrees with the theory of social balance because existing connections reflect the tendency of the new links emergence between individuals. And PR value is to measure the positive or negative tendency of edges without sign. The experiments on real big social data proved the feasibility and efficiency of the SPR model: Comparing with some popular predication methods, the SPR model in this issue shows lower complexity and higher accuracy. Experimental results also prove that the SPR model provide insight and foresight of the mechanism driving the sign formation of links.

show abstract

Section: Comparison Of Resultsmentioning

confidence: 99%

Section: Epinions Slashdot Wikipediamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sign Prediction on Social Networks Based Nodal Features

Zhu

2020

Complexity

View full text Add to dashboard Cite

show abstract

“…The details of the experimental datasets are given in Table 1. Accuracy and F1-score [38] are used to measure the sign prediction performances of the proposed method. Two baseline methods are used in this paper to compare with SP BBTL.…”

Section: Resultsmentioning

confidence: 99%

Sign Prediction on Unlabeled Social Networks Using Branch and Bound Optimized Transfer Learning

et al. 2019

Self Cite

View full text Add to dashboard Cite

Sign prediction problem aims to predict the signs of links for signed networks. Currently it has been widely used in a variety of applications. Due to the insufficiency of labeled data, transfer learning has been adopted to leverage the auxiliary data to improve the prediction of signs in target domain. Existing works suffer from two limitations. First, they cannot work if there is no target label available. Second, their generalization performance is not guaranteed due to that fact that the solution of their objective functions is not global optimal solution. To solve these problems, we propose a novel sign prediction on unlabeled social networks using branch and bound optimized transfer learning (SP_BBTL) sign prediction model. The main idea of SP_BBTL is to use target feature vectors to reconstruct source domain feature vectors based on relationship projection, which is a complicated optimal problem and is solved by proposed optimization based on branch and bound that can obtain global optimal solution. With this design, the target domain label information is not required for classifier. Finally, the experimental results on the large scale social signed networks validate the superiority of the proposed model.

show abstract

Socialized healthcare service recommendation using deep learning

Yuan

Guan

et al. 2018

Neural Comput & Applic

View full text Add to dashboard Cite

Negative sign prediction for signed social networks

Cited by 17 publications

References 19 publications

Sign Prediction on Social Networks Based Nodal Features

Sign Prediction on Social Networks Based Nodal Features

Sign Prediction on Unlabeled Social Networks Using Branch and Bound Optimized Transfer Learning

Socialized healthcare service recommendation using deep learning

Contact Info

Product

Resources

About