Experiments of Trust Prediction in Social Networks by Artificial Neural Networks

Graña, Manuel; Núñez-González, J. David; Ozaeta, Leire; Kamińska-Chuchmała, Anna

doi:10.1080/01969722.2015.1007725

Cited by 15 publications

(8 citation statements)

References 30 publications

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“…The cold start problem rises when there is no previous experience about the trustee behavior. Then, there is a need to predict the trust value [12] from indirect information, that is, from reputation information obtained from third party witness. Trust prediction can be formulated as a classification problem, where feature vectors are computed from the reputation information extracted from the network.…”

Section: Introductionmentioning

confidence: 99%

Reputation features for trust prediction in social networks

2015

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Section: Introductionmentioning

confidence: 99%

Reputation features for trust prediction in social networks

2015

Self Cite

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“…For example, it was used to create a model that predicts social security fraud detection in Belgium [ 86 ]. In another case the technique was utilized to reduce some of the effects of class imbalance among Trust and Distrust classes in social network online services [ 84 ]. Finally, it was employed to rebalance the classes for a trust prediction problem using social network data [ 85 ].…”

Section: Discussionmentioning

confidence: 99%

“…Depending upon the amount of over–sampling required, neighbours from the k nearest neighbours are randomly chosen” ([ 59 ] [p. 328]). SMOTE has been successfully used to balance classes in classification problems involving social network data [ 84 – 86 ]. Here, for the WC case SMOTE obtained 42 synthetic observations for class 1 and 70 for class 0 while in the CN case, the corresponding numbers were 84 and 140.…”

Section: Methodsmentioning

confidence: 99%

Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases

et al. 2016

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Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers–Logistic Regression, Naïve Bayes and Random Forest–with a range of social network measures and the necessary databases to model the verdicts in two real–world cases: the U.S. Watergate Conspiracy of the 1970’s and the now–defunct Canada–based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures.

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“…They followed this with a model to formulate the trust prediction and rating prediction problems. Grana [62] introduced a supervised trust prediction approach: a binary classification that focuses on users' reputation. Wang et al [63] proposed a trust-distrust prediction approach that simultaneously employed Dempster-Shafer theory and neural networks.…”

Section: ) Supervised Approachesmentioning

confidence: 99%

“…Supervised/Unsupervised Context-Aware Dynamic Moradi and Ahmadian [88] U N Y Sanadhy and Singh [89] U N Y Raj and Babu [73] U N N Zhao et al [74] S Y N Zhang et al [93] U Y Y Zhang et al [107] S Y N Zhang et al [75] S N N Zhang et al [108] S Y N Liu et al [109] U Y N Zheng et al [112] U Y N Matsutani et al [94] U N N Tang et al [95] U N Y Zhang and Yu [47] U N N Chakraverty et al [76] S N N Sacco and Breslin [56] S N N Huang et al [96] U N N Li and Wang [117] U Y N Fazeli et al [90] U N N Tang et al [92] U N N Moturu and Liu [97] U N N Nunez-Gonzalez et al [77] S N N Yao et al [98] U N N Huang et al [99] U N N Liu et al [37] S N Y Ma et alL [60] S N N Matsuo and Yamamoto [61] S N Y Grana et al [62] S N N Wang et al [63] S N N Bachi et al [66] S Y N Korovaiko and Thomo [68] S N N Borzymek and Sydow [69] S N N Laspez and Maag [70] S Y N Ghafari et al [65] S Y N Zolfaghar and Aghaie [72] S Y Y Tang et al [25] U N N Wang et al [82] U N N Ghafari et al [58] and [4] U Y N Guha et al [84] U N N Golbeck [85] U N N Wang et al [32] U N N Zheng et al …”

Section: Approachmentioning

confidence: 99%

A Survey on Trust Prediction in Online Social Networks

et al. 2020

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Level of Trust can determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for measuring trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust prediction is the process of predicting a new trust relation between two users who are not currently connected. In applications of trust, trust relations among users need to be predicted. This process faces many challenges, such as the sparsity of user-specified trust relations, the context-awareness of trust and changes in trust values over time. In this paper, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs, classify them based on different factors, and propose some future directions for researchers interested in this field.

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Experiments of Trust Prediction in Social Networks by Artificial Neural Networks

Cited by 15 publications

References 30 publications

Reputation features for trust prediction in social networks

Reputation features for trust prediction in social networks

Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases

A Survey on Trust Prediction in Online Social Networks

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