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User Behaviour Based Friend Recommendation in Facebook Social Networks

Sanjeev Dhawan1 , Kulvinder Singh2 , Honey Gupta3

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-6 , Page no. 486-490, Jun-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i6.486490

Online published on Jun 30, 2018

Copyright © Sanjeev Dhawan, Kulvinder Singh, Honey Gupta . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Sanjeev Dhawan, Kulvinder Singh, Honey Gupta, “User Behaviour Based Friend Recommendation in Facebook Social Networks,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.486-490, 2018.

MLA Style Citation: Sanjeev Dhawan, Kulvinder Singh, Honey Gupta "User Behaviour Based Friend Recommendation in Facebook Social Networks." International Journal of Computer Sciences and Engineering 6.6 (2018): 486-490.

APA Style Citation: Sanjeev Dhawan, Kulvinder Singh, Honey Gupta, (2018). User Behaviour Based Friend Recommendation in Facebook Social Networks. International Journal of Computer Sciences and Engineering, 6(6), 486-490.

BibTex Style Citation:
@article{Dhawan_2018,
author = {Sanjeev Dhawan, Kulvinder Singh, Honey Gupta},
title = {User Behaviour Based Friend Recommendation in Facebook Social Networks},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2018},
volume = {6},
Issue = {6},
month = {6},
year = {2018},
issn = {2347-2693},
pages = {486-490},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2209},
doi = {https://doi.org/10.26438/ijcse/v6i6.486490}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i6.486490}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2209
TI - User Behaviour Based Friend Recommendation in Facebook Social Networks
T2 - International Journal of Computer Sciences and Engineering
AU - Sanjeev Dhawan, Kulvinder Singh, Honey Gupta
PY - 2018
DA - 2018/06/30
PB - IJCSE, Indore, INDIA
SP - 486-490
IS - 6
VL - 6
SN - 2347-2693
ER -

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Abstract

Social networks provides platform to user to share their pictures, videos and make new friends and follow a community and so on. There are different applications of social networks but mostly used applications are Facebook, Instagram, and twitter. A user can recommend a page or community to other user based on their interests but it is difficult to recognize which page or posts posted on page is original or not for this in this paper an attempt has been made to recommend a friend to follow a Facebook page or not. In proposed mechanism the posts are distinguished based on their popularity which is calculated various features like comments reactions shares. this popularity is calculated using python program. The proposed mechanism is analyzed using Gephi with performance metrics like modularity, centrality betweeness, page rank etc.

Key-Words / Index Term

Social Networks, Facebook, User, Posts Popularity, Netvizz and Gephi.

References

[1]. J. Staiano, F. Pianesi, B. Lepri, and A. Pentland, “Friends don t Lie - Inferring Personality Traits from Social Network Structure,” in Proceedings of the 2012 ACM conference on ubiquitous computing, 2012, pp. 321–330.
[2]. Pran Dev, Jyoti, Dr. Kulvinder Singh and Dr. Sanjeev Dhawan, “A Naive Algorithmic Approach for Detection of Users’ with Unusual Behavior in online Social Networks” International Journal of Software and Web Sciences (IJSWS), ISSN: 2279-0071pp: 37-41,2015.
[3]. Ekta, Sanjeev Dhawan and Kulvinder Singh, “Feature Extraction and Content Investigation of Facebook User’s using Netvizz and Gephi”, Advances in Computer Science and Information Technology (ACSIT), ACSIT 2016, pp. 262-265.
[4]. Kyungmin Kim, TaehunKimand Soon J. Hyun, “Friend Recommendation using Offline and Online Social Information for Face-to-Face Interactions”, IEEE 2016, pp: 1-5.
[5]. Fenghua Li, Yuanyuan He, Ben Niu, Hui Li and Hanyi Wang, “Match-MORE: An Efficient Private Matching Scheme Using Friends-of-Friends’ Recommendation”, 2016 IEEE International Conference on Computing, Networking and Communications, Communications and Information Security, pp: 1-6.
[6]. Sanjeev Dhawan andShiviGoel, “Analysis of Pattern of Information Revelation and Site Use Behavior in Social Networking Sites”,International Journal of Computer Applications Technology and Research 2014 ISSN: 2319–8656 pp: 42 – 44.
[7]. Sanjeev Dhawan, Kulvinder Singh and Jyoti, “High Rating Recent Preferences Based Recommendation System”,4thInternational Conference on Eco-friendly Computing and Communication Systems 2015,pp: 259-264.
[8]. Bolong Cui “A friend recommendation algorithm based on trajectory mining”, 9th International Symposium on Computational Intelligence and Design,I EEE 2016, pp:338-341.
[9]. Rui Ding, Jia Zhu, Yong Tang, Xueqin Lin, Danyang Xiao and Haoye Dong, “A Novel Feature Selection Strategy for Friends Recommendation”, Proceedings of the 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design,pp:123-128.
[10]. Yuanyuan He, Fenghua Li, Ben Niu and Jiafeng Hua, “Achieving Secure and Accurate Friend Discovery Based on Friend-of-Friend’s Recommendations”, IEEE ICC2016 Communication and Information Systems Security Symposium, pp: 1-6.
[11]. XiaoweiJia, XiaoyiLiy, Kang Liy, VishrawasGopalakrishnany, GuangxuXuny and Aidong Zhang, “Collaborative Restricted Boltzmann Machine for Social Event Recommendation”, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp: 402-405.
[12]. Zigui Jiang, Ao Zhou, Shangguang Wang, Qibo Sun, Rongheng Lin and Fangchun Yang, “Personalized Service Recommendation for Collaborative Tagging Systems with Social Relations and Temporal Influences”, 2016 IEEE International Conference on Services Computing, pp: 786-789.
[13]. Fan Jiang, Carson K. Leung and Adam G. M. Pazdor, “Big Data Mining of Social Networks for Friend Recommendation”, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM),pp: 921-922.
[14]. https://docs.python.org/3/tutorial/index.html accessed on 21-06-18.
[15]. https://gephi.org/users/tutorial-visualization/ accessed on 21-06-18.
[16]. Sanjeev Dhawan, Kulvinder Singh, Honey Gupta “User Behavior Based Friend Recommendation in Facebook Social Networks” ,2018, International Journal of Computer Sciences and Engineering, vol 06, issue 03, pp 69-73,