User relationship index based on social network community analysis

Social network has become a very popular way for internet users to communicate and interact online. The socia; networks provide a platform to maintain a contact with friends. Increasing social network’s popularity allows all of them to collect large amounts of personal details about their users. Globally, the issue of identifying spammers have received great attention due to its practical relevance in the field of social network analysis. Social network community users are fed with irrelevant information while surfing, due to spammer's activity. Spam pervades any information system such as e-mail or web, social, blog or reviews platform. The aim of this paper is to examine previous works in the field of spam detection in social networks, the study attempts to review various spam detection frameworks which details about the detection and elimination of spam's in various sources, By classification and Clustering Method of spam detection and by raising security awareness among the users of social networks and stake holders , by prescribing a strategic approach or data mining approach for analyzing the nature of spam detection on social networks.

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Weak Connection Edges Independent Discriminant of Rapid Spanning Tree Recommendation of Social Network Community

International Journal of Multimedia and Ubiquitous Engineering ◽

10.14257/ijmue.2016.11.3.20 ◽

2016 ◽

Vol 11 (3) ◽

pp. 209-222

Author(s):

Cao Ke-xing ◽

Liu Zhao-xing ◽

Li Xin ◽

Lv Zhi-han

Keyword(s):

Social Network ◽

Spanning Tree ◽

Network Community

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A Novel Method for Mining the Advisor-Student Relationships in Academic Social Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.655-657.1795 ◽

2013 ◽

Vol 655-657 ◽

pp. 1795-1799

Author(s):

Yue Wang ◽

Xiao Lin Liu

Keyword(s):

Social Network ◽

Community Analysis ◽

Research Field ◽

Markov Logic ◽

Student Relationships ◽

Novel Method ◽

Academic Social Network ◽

Student Relationship ◽

Markov Logic Network ◽

The Relationship

Academic social network contains abundant knowledge about relationships among people or entities. Building the relationship between different entities correctly can help providing comprehensive services in the scientific research field. Unfortunately, some relationships, such as advisor-student relationship, are often hidden in academic social network, which are not explicitly categorized. Discovery of these relationships can benefit many valuable applications such as research community analysis. In this paper, a novel method based on Markov Logic Network is proposed to mine the advisor-student relationship in academic social network. Experimental results show that the proposed approach can find the advisor-student relationship effectively.

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Trust Analysis in Social Network Community Research

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.55-57.1578 ◽

2011 ◽

Vol 55-57 ◽

pp. 1578-1583

Author(s):

Bing Wu ◽

Wen Xia Xu ◽

Jun Ge

Keyword(s):

United States ◽

Social Network ◽

Recommendation System ◽

The United States ◽

Community Research ◽

Future Research ◽

Research Development ◽

Main Research ◽

Network Community ◽

Popular Language

This study is a productivity review on the literature gleaned from SSCI, SCIE databases concerning trust analysis in social network community research. The result indicates that the number of literature productions on trust analysis in social network community is still growing. The main research development country is the United States, and from the analysis of the distribution of language, English is the most popular language. Moreover the research focuses on are mainly empirical research, computational model and recommendation system, we analyze these typical references in detail, also limitations and future research.

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