research-article

DCAT: A Deep Context-Aware Trust Prediction Approach for Online Social Networks

Authors:

Seyed Mohssen Ghafari,

Shahpar Yakhchi,

Mehmet OrgunAuthors Info & Claims

MoMM2019: Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia

Pages 20 - 27

https://doi.org/10.1145/3365921.3365940

Published: 22 February 2020 Publication History

Abstract

Customer reviews are now increasingly available on Online Social Networks (OSNs) for a wide range of products and services. Trust in the review's author is a crucial basis for believing in the reliability of reviews generated on such networks. In this context, the main challenge is to predict the unknown trust relationship between two users. Existing trust prediction approaches fail to incorporate textual footprint of users. To address this challenge, we present a deep learning-based graph analytics model to predict trust relations in OSNs. We leverage and extend GraphSAGE, a method for computing node representations in an inductive manner, to develop a deep classifier. We present our experiment with datasets from review websites to train classifiers that predict trust relations between pairs of users, and highlight how our approach significantly improves the quality of predicted trust relations compared to the state-of-the-art approaches.

References

[1]

[n.d.]. https://pypi.org/project/stellargraph/. ([n.d.]).

[2]

S. A.Haija, A. Kapoor, B. Perozzi, and J. Lee. 2018. N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. CoRR (2018).

[3]

Irwin Altman and Dalmas Taylor. 1973. Social penetration: The development of interpersonal relationships. In The Journal of Social Psychology. 79--90.

[4]

Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral networks and locally connected networks on graphs. In International Conference on Learning Representations (ICLR).

[5]

Scott Deeann Chen, Ying-Yu Chen, Jiawei Han, and Pierre Moulin. 2013. A Feature-Enhanced Ranking-Based Classifier for Multimodal Data and Heterogeneous Information Networks. In IEEE 13th International Conference on Data Mining, USA. 997--1002.

[6]

S. M. Platek D. S. Gordon. 2009. Trustworthy? The brain knows: Implicit neural responses to faces that vary in dark triad personality characteristics and trustworthiness. Journal of Social, Evolutionary, and Cultural Psychology 3 (2009), 182--200.

[7]

CSIRO's Data61. 2018. StellarGraph Machine Learning Library. https://github.com/stellargraph/stellargraph.

[8]

Michael Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems (2016), 3844âĂŞ3852.

[9]

Stephanie N. Downey, van der Werff, Kecia M., and Victoria C. 2015. The role of diversity practices and inclusion in promoting trust and employee engagement. Journal of Applied Social Psychology 45, 1 (2015), 35--44.

[10]

S.N. Eisenstadt. 1965. Exchange and power in social life. Journal of Sociology 49 (1965).

[11]

SM Ghafari and C. Tjortjis. 2019. A survey on association rules mining using heuristics. WIREs Data Mining Knowl Discov (WIREs) 9:e1307 (2019).

[12]

S. M. Ghafari, M. Fazeli, A. Patooghy, and L. Rikhtechi. 2013. Bee-MMT: A load balancing method for power consumption management in cloud computing. In Sixth International Conference on Contemporary Computing (IC3), India. 76--80.

[13]

Seyed Mohssen Ghafari, Shahpar Yakhchi, Amin Beheshti, and Mehmet Orgun. 2018. SETTRUST: Social Exchange Theory Based Context-Aware Trust Prediction in Online Social Networks. In The 5th International Workshop on Data Quality and Trust in Big Data (QUAT 2018).

[14]

Seyed Mohssen Ghafari, Shahpar Yakhchi, Amin Beheshti, and Mehmet Orgun. 2018. Social Context-Aware Trust Prediction: Methods for Identifying Fake News. In Web Information Systems Engineering - WISE 2018 - 19th International Conference, Dubai, United Arab Emirates, November 12-15, 2018, Proceedings, Part I (Lecture Notes in Computer Science), Hakim Hacid, Wojciech Cellary, Hua Wang, Hye-Young Paik, and Rui Zhou (Eds.), Vol. 11233. Springer, 161--177.

[15]

J. Golbeck. 2006. Using Trust and Provenance for Content Filtering on the Semantic Web. In Proceedings of the Workshop on Models of Trust on the Web, at the 15th World Wide Web (WWW) conf.

[16]

Jennifer Golbeck. 2009. Trust and Nuanced Profile Similarity in Online Social Networks. ACM Trans. Web 3 (2009), 12:1--12:33.

Digital Library

[17]

Jennifer Golbeck, Bijan Parsia, and James A. Hendler. 2003. Trust Networks on the Semantic Web. In Cooperative Information Agents VII (CIA). 238--249.

[18]

I. Goodfellow, Y. Bengio, and A. Courville. 2016. Deep Learning. MIT Press.

[19]

Ramanathan V. Guha, Ravi Kumar, Prabhakar Raghavan, and Andrew Tomkins. 2004. Propagation of trust and distrust. In 13th international conference on World Wide Web, WWW, USA,. 403--412.

Digital Library

[20]

W. L. Hamilton, R. Ying, and J. Leskovec. 2017. Inductive Representation Learning on Large Graphs. CoRR (2017).

[21]

S. Hochreiter and J. Schmidhuber. 1997. Long short-term memory. Neural Computation (1997).

[22]

Young Ae Kim, Minh-Tam Le, Hady Wirawan Lauw, Ee-Peng Lim, Haifeng Liu, and Jaideep Srivastava. 2008. Building a web of trust without explicit trust ratings. In ICDE. 531--536.

[23]

Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR). 729--734.

[24]

Guanfeng Liu, Yi Liu, An Liu, Zhixu Li, Kai Zheng, Yan Wang, and Xiaofang Zhou. 2018. Context-aware trust network extraction in large-scale trust-oriented social networks. World Wide Web 21 (2018), 713--738.

Digital Library

[25]

Guanfeng Liu, Yan Wang, and Mehmet A. Orgun. 2012. Social Context-Aware Trust Network Discovery in Complex Contextual Social Networks. In AAAI. 101--107.

[26]

Haifeng Liu, Ee-Peng Lim, Hady Wirawan Lauw, Minh-Tam Le, Aixin Sun, Jaideep Srivastava, and Young Ae Kim. 2008. Predicting trusts among users of online communities: an epinions case study. In 9th ACM Conference on Electronic Commerce (EC), USA. 310--319.

Digital Library

[27]

Xin Liu. 2013. Towards Context-Aware Social Recommendation via Trust Networks. In Web Information Systems Engineering - WISE - 14th International Conference, Nanjing, China, Proceedings, Part I (Lecture Notes in Computer Science), Vol. 8180. Springer, 121--134.

[28]

Xin Liu and Anwitaman Datta. 2012. Modeling Context Aware Dynamic Trust Using Hidden Markov Model. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, Toronto, Ontario, Canada. AAAI Press.

Digital Library

[29]

Nan Ma, Ee-Peng Lim, Viet-An Nguyen, Aixin Sun, and Haifeng Liu. 2009. Trust relationship prediction using online product review data. In CIKM-CNIKM. 47--54.

[30]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States. 3111--3119.

Digital Library

[31]

Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, and Michael M. Bronstein. 2016. Geometric deep learning on graphs and manifolds using mixture model CNNs. CoRR abs/1611.08402 (2016).

[32]

Surya Nepal, Wanita Sherchan, and Cécile Paris. 2011. STrust: A Trust Model for Social Networks. In TrustCom. 841--846.

[33]

James Pennebaker, Cindy Chung, and Molly Ireland. 2007. Development and psychometric properties of LIWC. In LIWC.net, Austin, Texas 78703 USA.

[34]

Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation. In Empirical Methods in Natural Language Processing (EMNLP).

[35]

Manoel Horta Ribeiro, Pedro H. Calais, Yuri A. Santos, Virgílio A. F. Almeida, and Wagner Meira Jr. 2018. Characterizing and Detecting Hateful Users on Twitter. In Proceedings of the Twelfth International Conference on Web and Social Media, ICWSM, Stanford, California, USA. 676--679.

[36]

M. Richardson, R. Agrawal, and P. Domingos. 2003. Trust Management for the Semantic Web. In The Semantic Web - ISWC.

[37]

Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann. 2018. Pitfalls of Graph Neural Network Evaluation. CoRR (2018).

[38]

Wanita Sherchan, Surya Nepal, and Cécile Paris. 2013. A survey of trust in social networks. ACM Comput. Surv. 45 (2013).

[39]

Chris Sumner, Alison Byers, Rachel Boochever, and Gregory J. Park. 2012. Predicting Dark Triad Personality Traits from Twitter Usage and a Linguistic Analysis of Tweets. In 11th International Conference on Machine Learning and Applications, ICMLA, Boca Raton, FL, USA, December 12-15, 2012. Volume 2. IEEE, 386--393.

[40]

Jiliang Tang, Huiji Gao, Xia Hu, and Huan Liu. 2013. Exploiting homophily effect for trust prediction. In International Conference on Web Search and Data Mining, WSDM, Italy. 53--62.

Digital Library

[41]

J. Tang and H. Liu. 2015. Trust in Social Media. Synthesis Lectures on Information Security, Privacy, and Trust (2015).

[42]

Mohammad Gias Uddin, Mohammad Zulkernine, and Sheikh Iqbal Ahamed. 2008. CAT: a context-aware trust model for open and dynamic systems. In Proceedings of the ACM Symposium on Applied Computing (SAC), Fortaleza, Ceara, Brazil, Roger L. Wainwright and Hisham Haddad (Eds.). ACM, 2024--2029.

Digital Library

[43]

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR, Vancouver, BC, Canada, Conference Track Proceedings.

[44]

Yating Wang, Ing-Ray Chen, Jin-Hee Cho, Ananthram Swami, Yen-Cheng Lu, Chang-Tien Lu, and Jeffrey J. P. Tsai. 2018. CATrust: Context-Aware Trust Management for Service-Oriented Ad Hoc Networks. IEEE Trans. Services Computing 11, 6 (2018), 908--921.

[45]

Yan Wang, Lei Li, and Guanfeng Liu. 2015. Social context-aware trust inference for trust enhancement in social network based recommendations on service providers. World Wide Web 18 (2015), 159--184.

Digital Library

[46]

Ying Wang, Xin Wang, Jiliang Tang, Wanli Zuo, and Guoyong Cai. 2015. Modeling Status Theory in Trust Prediction. In 21th AAAI Conference on Artificial Intelligence, USA. 1875--1881.

[47]

M. Yakhchi, S. M. Ghafari, S. Yakhchi, M. Fazeli, and A. Patooghi. 2015. Proposing a load balancing method based on Cuckoo Optimization Algorithm for energy management in cloud computing infrastructures. In 6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), Istanbul. 1--5.

[48]

S. Yakhchi, A.Beheshti, S.M. Ghafari, and M. Orgun. 2019. Enabling the Analysis of Personality Aspects in Recommender Systems. In Pacific Asia Conf. on Info. Systems (PACIS), China.

[49]

Shahpar Yakhchi, Seyed Mohssen Ghafari, and Amin Beheshti. 2018. CNR: Cross-network Recommendation Embedding User's Personality. In Data Quality and Trust in Big Data. 62--77.

[50]

S. Yakhchi, S. M. Ghafari, M. Yakhchi, M. Fazeli, and A. Patooghy. 2015. ICA-MMT: A load balancing method in cloud computing environment. In 2nd World Symposium on Web Applications and Networking(WSWAN), Sousse. 1--7.

[51]

R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec. 2018. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In Proceedings of the 24th ACM SIGKDD (KDD '18) International Conference on Knowledge Discovery & Data Mining.

[52]

Yu Zhang, Huajun Chen, and Zhaohui Wu. 2006. A Social Network-Based Trust Model for the Semantic Web. In Autonomic and Trusted Computing (ATC). 183--192.

[53]

Xiaoming Zheng, Yan Wang, Mehmet A. Orgun, Guanfeng Liu, and Haibin Zhang. 2014. Social Context-Aware Trust Prediction in Social Networks. In International Conference on Service-Oriented Computing (ICSOC). 527--534.

[54]

Kiyana Zolfaghar and Abdollah Aghaie. 2012. A Syntactical Approach for Interpersonal Trust Prediction in Social Web Applications: Combining Contextual and Structural Data. Know.-Based Syst. 26 (2012), 93--102.

Digital Library

Cited By

Shetty NMuniyal BMaben LJayaraj RSaxena S(2024)Dynamic Twitter friend grouping based on similarity, interaction, and trust to account for ever‐evolving relationshipsIET Communications10.1049/cmu2.12807Online publication date: 26-Jul-2024
https://doi.org/10.1049/cmu2.12807
Li ZFang WZhu CChen WHao TZhang W(2024)Trust Evaluation with Deep Learning in Online Social Networks: A State-of-the-Art ReviewAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5588-2_1(3-12)Online publication date: 13-Aug-2024
https://doi.org/10.1007/978-981-97-5588-2_1
Pendyala V(2022)Misinformation Containment Using NLP and Machine LearningDeep Learning Research Applications for Natural Language Processing10.4018/978-1-6684-6001-6.ch003(41-56)Online publication date: 9-Dec-2022
https://doi.org/10.4018/978-1-6684-6001-6.ch003
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DCAT: A Deep Context-Aware Trust Prediction Approach for Online Social Networks

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MoMM2019: Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia

December 2019

266 pages

ISBN:9781450371780

DOI:10.1145/3365921

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Johannes Kepler University, Linz, Austria
@WAS: International Organization of Information Integration and Web-based Applications and Services

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Association for Computing Machinery

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Publication History

Published: 22 February 2020

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MoMM2019: The 17th International Conference on Advances in Mobile Computing & Multimedia

December 2 - 4, 2019

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Cited By

Shetty NMuniyal BMaben LJayaraj RSaxena S(2024)Dynamic Twitter friend grouping based on similarity, interaction, and trust to account for ever‐evolving relationshipsIET Communications10.1049/cmu2.12807Online publication date: 26-Jul-2024
https://doi.org/10.1049/cmu2.12807
Li ZFang WZhu CChen WHao TZhang W(2024)Trust Evaluation with Deep Learning in Online Social Networks: A State-of-the-Art ReviewAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5588-2_1(3-12)Online publication date: 13-Aug-2024
https://doi.org/10.1007/978-981-97-5588-2_1
Pendyala V(2022)Misinformation Containment Using NLP and Machine LearningDeep Learning Research Applications for Natural Language Processing10.4018/978-1-6684-6001-6.ch003(41-56)Online publication date: 9-Dec-2022
https://doi.org/10.4018/978-1-6684-6001-6.ch003
Gao XXu WLiao MChen G(2021)Trust Prediction for Online Social Networks with Integrated Time-Aware SimilarityACM Transactions on Knowledge Discovery from Data10.1145/344768215:6(1-30)Online publication date: 19-May-2021
https://dl.acm.org/doi/10.1145/3447682
Al Jawarneh IBellavista PCorradi AFoschini LMontanari R(2021)Context Incorporation Techniques for Social Recommender SystemsICC 2021 - IEEE International Conference on Communications10.1109/ICC42927.2021.9500434(1-6)Online publication date: Jun-2021
https://doi.org/10.1109/ICC42927.2021.9500434
Yakhchi SGhafari SOrgun M(2021)TAP: A Two-Level Trust and Personality-Aware Recommender SystemService-Oriented Computing – ICSOC 2020 Workshops10.1007/978-3-030-76352-7_30(294-308)Online publication date: 30-May-2021
https://doi.org/10.1007/978-3-030-76352-7_30
Ghafari SBeheshti AJoshi AParis CYakhchi SJolfaei AOrgun M(2020)A dynamic deep trust prediction approach for online social networksProceedings of the 18th International Conference on Advances in Mobile Computing & Multimedia10.1145/3428690.3429167(11-19)Online publication date: 30-Nov-2020
https://dl.acm.org/doi/10.1145/3428690.3429167
Ghafari SBeheshti AJoshi AParis CMahmood AYakhchi SOrgun M(2020)A Survey on Trust Prediction in Online Social NetworksIEEE Access10.1109/ACCESS.2020.30094458(144292-144309)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3009445

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