@article {10.3844/jcssp.2014.578.584,
article_type = {journal},
title = {NEURO FUZZY LINK BASED CLASSIFIER FOR THE ANALYSIS OF BEHAVIOR MODELS IN SOCIAL NETWORKS},
author = {Ponnuvel, Indira Priya and Kumar, Ghosh Dalim and Arputharaj, Kannan and Sannasi, Ganapathy},
volume = {10},
number = {4},
year = {2013},
month = {Dec},
pages = {578-584},
doi = {10.3844/jcssp.2014.578.584},
url = {https://thescipub.com/abstract/jcssp.2014.578.584},
abstract = {In this study, a new link based classifier using neuro fuzzy logic has been proposed for analyzing the social behavior based on Weblog dataset. In this system, data are processed using a multistage structure. This system provides a diagnosis using a neuro fuzzy link based classifier that analyses the user’s behavior to specific diagnostic categories based on their cluster category in social networks. It uses random walks method to organize the labels. Since the links present in the social network graph frequently represent relationships among the users with respect to social contacts and behaviours, this work observes the links of the graph in order to identify the relationships represented in the graph between the users of the social network based on some new social network metrics and the past behaviour of the users. This work is useful to provide connection between consolidated features of users based on network data and also using the traditional metrics used in the analysis of social network users. From the experiments conducted in this research work, it is observed that the proposed work provides better classification accuracy due to the application of neuro fuzzy classification method in link analysis.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}