• P.Sathish Kumar Associate Professor, K.S.Rangasamy College of Technology, India
  • T.Suvathi PG Student, K.S.Rangasamy College of Technology, India



Communal, FGM Algorithm, Decision Tree


Communal is one of the common words. A billion of peoples share or have certain attitudes and interests in common. By sharing and receiving the information such as text, image, audio, video etc., this kind of information, it improves the global knowledge, easily distinct the good and bad things. Now days, Social media is a good platform to share the content collectively with collaboration. Digital technologies are spread all over the global rapidly. It is an efficient way to improve the knowledge via Communal. People do not show the attention to join the community because of addiction, hacks the personal data and get misused. So that people have lack of awareness to join and use the communities. To overcome the above reasons and also all the peoples have to access and gain the information without any dilemmas. The proposed system provides the platform to link the peoples via Communal much more and gather the information all over the world with secure authentication. Anywhere in the world, every person can share and learn their thoughts with everyone. It consist of two phase to implement the proposed system. The first phase is to identify the neighbourhood and link the data. Here use Interest based FGM algorithm to predict the neighbour and link within the environment. So that each person will know all the information. Second phase, decision process to detect the person who are all link with particular communities across globally with the help of decision tree. People from anywhere to access all the data with anyone. It is easy way to equip people in all kind of innovative ideas as soon as possible.


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Ainuddin Wahid Abdul Wahab, Ghulam Mujtaba And Mohammed Ali Al-Garadi (2016), “Virtual Community Detection Through the Association between Prime Nodes in Online Social Networks and Its Application to Ranking Algorithms”, IEEE, vol.4, no.2, pp. 9614-9624.

Ajay Kumar Singh Kushwah and Amit Kumar Manjhvar (2016), “A Review on Link Prediction in Social Network”, International Journal of Grid and Distributed Computing Vol. 9, No. 2, pp.43-50. DOI:

Arun Kumar Sangaiah, Chaoqin Zhang And Jiangtao Ma (2017),“ Balancing User Profile and Social Network Structure for Anchor Link Inferring Across Multiple Online Social Networks”, IEEE, vol.5, no.4, pp. 12031-12039.

Bai Wang, Bin Wu and Le Yu (2013), “LBLP: Link-Clustering-Based Approach for Overlapping Community Detection”, Tsinghua Science and Technology, vol.18, no.3,pp.387-397.

Chuan Shi, Jiawei Zhang, Philip S. Yu, Yitong Li and Yizhou Sun (2015), “A Survey of Heterogeneous Information Network Analysis”, IEEE, vol.8, no.1, pp. 751-771.

Chungmok Lee, Dennis K. J. Lin, Minh Pham and Norman Kim (2014), “A Novel Link Prediction Approach for Scale-free Networks”, International World Wide Web Conference Committee.

Diane Gan, George Loukas and Ryan Heartfield (2016), “You Are Probably Not the Weakest Link: Towards Practical Prediction of Susceptibility to Semantic Social Engineering Attacks” IEEE, vol.4, no.4, pp. 6910-6928.

Fan Yang, Qingshuang Sun, Rongjing Hu and Zhao Yang (2017),” An Improved Link Prediction Algorithm Based on Degrees and Similarities of Nodes”, IEEE, vol.24, no.3, pp. 978-985.

Feng Tan, Yunlong Guo and Zheyu Zhang (2013), “Latent Co-interests’ Relationship Prediction”, Tsinghua Science and Technology, vol.18, no.4,pp.379-386. DOI:

Francesco Bonchi, Giuseppe Manco and Nicola Barbieri (2014), “Who to Follow and Why:Link Prediction with Explanations”, IEEE, vol.7, pp. 24-27.

Guangwu Hu, Rui Zha, Yaqiong Qiao and Yongzhong Huang (2016), “De-anonymizing Social Networks with Random Forest Classifier”, IEEE, vol.8, pp. 284-290.

Haifang Li, Hao Guo, Tian Tian and Yanli Yang (2015), “Link Prediction in Brain Networks Based on a Hierarchical Random Graph Model”, Tsinghua Science and Technology, vol.20, no.3,pp. 306-315. DOI:

Haiwei Pan, Shuai Han, Xiaoqin Xie, Yijia Li and Zhiqiang Zhang (2015),”A Joint Link Prediction Method for Social Network”, Springer-Verlag Berlin Heidelberg, pp. 56–64.

Hongjuan Li, Ruinian Li, Xiaobo Zhou and Xiuzhen Cheng (2017), “Perturbation-Based Private Profile Matching in Social Networks”, IEEE, vol.5, pp. 19720-19732. DOI:

Huan Xu, Liangwei Wang, Wenhuang Liu and Yujiu Yang (2013), “Node Classification in Social Network via a Factor Graph Model”, Springer-Verlag, pp. 213–224. DOI:

Jiawei Hanı, Nitesh Chawla, Yang Yang and Yizhou Sun (2012), “Predicting Links in MultiRelational and Heterogeneous Networks”, IEEE, International Conference on Data Mining, vol.10, no.3, pp. 755-764.

Koji Eguchi and Yosuke Sakata (2016), “Cross-lingual Link Prediction Using Multimodal Relational Topic Models”, IEEE, vol.12, pp. 26-29.

Quanqing Xu, , Wenli Ji, Yongjun Li And Zhen Zhang (2017), “User Identification Based on Display Names Across Online Social Networks”, IEEE, vol.5, pp. 17342-17353.

Sharma.D, Sharma.U, and Sunil Kumar Khatri (2014), “An Experimental Comparison of the Link Prediction Techniques in Social Networks”, vol. 1, pp. 321-329.

WANG Peng, WU YuRong, XU BaoWen and ZHOU XiaoYu (2015), “Link Prediction in Social Networks: the State-of-the-Art”, IEEE, Vol. 58, pp.1-13.




How to Cite

Kumar, P., & Suvathi, T. (2017). AN OPTIMAL FORECASTING OF LATENT SIMILARITY BASED RELATIONSHIP ON USER ATTRIBUTES USING LINK PREDICTION . International Journal of Engineering Technologies and Management Research, 4(11), 43–47.