Hai Van Pham
ABSTRACT
User identifications are in searching Online Social Networks (OSN) to find identical users among different social sites in many data sources (data integration, data enrichment, information retrieval,...). However, these user-unique attributes are difficult to obtain due to privacy issues. It is hard to identify users across multiple OSNs online. This paper has presented user's identification across multiple OSNs in order to develop searching engine for user identification. The proposed approach is designed to find by searching engine while accommodating User identifications in searching Online Social Networks (OSN). Experimental results demonstrate that our proposed approach achieves a significant improvement in term of performance accuracy.
- F. Khorasani, R. Gupta, and L. N. Bhuyan. 2015. Scalable SIMD-Efficient Graph Processing on GPUs. https://github.com/farkhor/PaRMAT. In 2015 International Conference on Parallel Architecture and Compilation (PACT). 39--50. https://doi. org/10.1109/PACT.2015.15Google Scholar
- Yongjun Li, Zhaoting Su, Jiaqi Yang, and Congjie Gao. 2020. Exploiting similarities of user friendship networks across social networks for user identification. Information Sciences 506, 244 (September 2020), 78--98.Google ScholarDigital Library
- P Moore and Hai V Pham. 2012. Intelligent Context with Decision Support under Uncertainty. In Proc of The 6th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS 2012). IEEE, 977--982.Google ScholarDigital Library
- P Moore and Hai V Pham. 2012. Towards Implementing Emotion into Intelligent Context. In Proc of The 13th International Conference on Web Information Systems Engineering (WISE 2012), The International Workshop on Advanced Reasoning Technology for e-Science (ART2012), Vol. LNCS 7652. Springer, 27--40.Google Scholar
- Philip T. Moore and Hai V. Pham. 2015. Personalization and rule strategies in data-intensive intelligent context-aware systems. The Knowledge Engineering Review 30, 2 (2015), 140--156. https://doi.org/10.1017/S0269888914000265Google ScholarCross Ref
- Hai Van Pham, PhilipMoore, and Khang Dinh Tran. 2014. Context Matching with Reasoning and Decision Support using Hedge Algebra with Kansei Evaluation. In Proc of the fifth symposium on Information and Communication Technology (SoICT 2014). ACM, Hanoi, Vietnam, 202--210.Google ScholarDigital Library
- Elie Raad, Richard Chbeir, and Albert Dipanda. 2010. User profile matching in social networks. In Network-Based Information Systems (NBiS). Japan, 297--304. https://hal.archives-ouvertes.fr/hal-00643509Google Scholar
- Wikipedia contributors. 2018. Forward chaining -- Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Forward_chaining&oldid= 828488734. [Online; accessed 26-August-2019].Google Scholar
- Wikipedia contributors. 2019. Graph theory -- Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Graph_theory&oldid= 905668677 [Online; accessed 26-August-2019].Google Scholar
- Wikipedia contributors. 2019. PageRank -- Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=PageRank&oldid=912546150. [Online; accessed 26-August-2019].Google Scholar
- Zhongbao Zhang, Qihang Gu, Tong Yue, and Sen Su. 2017. Identifying the same person across two similar social networks in a unified way: Globally and locally. Information Sciences 394--395 (2017), 53--67. https://doi.org/10.1016/j.ins.2017. 02.008Google ScholarDigital Library
- Z. Zhang, L. Sun, S. Su, J. Qu, and G. Li. 2019. Reconciling Multiple Social Networks Effectively and Effciently: An Embedding Approach. IEEE Transactions on Knowledge and Data Engineering (2019), 1--1. https://doi.org/10.1109/TKDE. 2019.2929786Google Scholar
Index Terms
- A Novel Approach using Context Matching Algorithm and Knowledge Inference for User Identification in Social Networks
Recommendations
Characterizing user behavior in online social networks
IMC '09: Proceedings of the 9th ACM SIGCOMM conference on Internet measurementUnderstanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present a first ...
Characterizing user navigation and interactions in online social networks
Understanding how users navigate and interact when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we ...
Social exchange in online social networks. The reciprocity phenomenon on Facebook
Our research is focused on reciprocity, which is crucial for social exchanges.The online social network platform of our choice was Facebook, which is one of the most successful online social sites.In our study we found strong empirical evidence that an ...
Comments