A Review of Different Machine Learning Models to Analyze Collective Behavior in Social Networks
M. Jeevana Sujitha1, P. Udayaraju2, V. Anjani Kranthi3

1M. Jeevana Sujitha, Assistant Professor, Department of CSE, SRKR, Bhimavaram (Andhra Pradesh), India.
2P. Udayaraju, Assistant Professor, Department of CSE, SRKR, Bhimavaram (Andhra Pradesh), India.
3V. Anjani Kranthi, Assistant Professor, Department of CSE, SRKR, Bhimavaram (Andhra Pradesh), India.
Manuscript received on 26 March 2019 | Revised Manuscript received on 07 April 2019 | Manuscript Published on 18 April 2019 | PP: 799-804 | Volume-7 Issue-6S March 2019 | Retrieval Number: F03560376S19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In social networks, Collective behavior defines individual user or human behavior whenever they are exposed different types tasks in outside environments like social networks. Different types of social networks like face book, twitter and you tube are used to describe prediction of collective behavior of different users. So that in this paper, we describe basic study regarding different approaches used to predict behavior of users in different social dimensions. This paper also describes how social networks can be used to describe and predict sequential human behavior at his/her individual selection or preference. This paper presents different behavior patterns in online social networks, and also describes other tasks present in social networks with their recommendations and advertising perspective data analysis.
Keywords: Social Related Networks, Prediction, Social Dimensions, user Behavior and Clustering.
Scope of the Article: Social Networks