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A Comprehensive Survey of Time Series Anomaly Detection in Online Social Network Data

by Md Rafiqul Islam, Naznin Sultana, Mohammad Ali Moni, Prohollad Chandra Sarkar, Bushra Rahman
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 3
Year of Publication: 2017
Authors: Md Rafiqul Islam, Naznin Sultana, Mohammad Ali Moni, Prohollad Chandra Sarkar, Bushra Rahman
10.5120/ijca2017915989

Md Rafiqul Islam, Naznin Sultana, Mohammad Ali Moni, Prohollad Chandra Sarkar, Bushra Rahman . A Comprehensive Survey of Time Series Anomaly Detection in Online Social Network Data. International Journal of Computer Applications. 180, 3 ( Dec 2017), 13-22. DOI=10.5120/ijca2017915989

@article{ 10.5120/ijca2017915989,
author = { Md Rafiqul Islam, Naznin Sultana, Mohammad Ali Moni, Prohollad Chandra Sarkar, Bushra Rahman },
title = { A Comprehensive Survey of Time Series Anomaly Detection in Online Social Network Data },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2017 },
volume = { 180 },
number = { 3 },
month = { Dec },
year = { 2017 },
issn = { 0975-8887 },
pages = { 13-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number3/28780-2017915989/ },
doi = { 10.5120/ijca2017915989 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:59:36.485306+05:30
%A Md Rafiqul Islam
%A Naznin Sultana
%A Mohammad Ali Moni
%A Prohollad Chandra Sarkar
%A Bushra Rahman
%T A Comprehensive Survey of Time Series Anomaly Detection in Online Social Network Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 3
%P 13-22
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the field of data mining, the social network is one of the complex systems that poses significant challenges in this area. Time series anomaly detection is one of the critical applications. Recent developments in the quantitative analysis of social networks, based largely on graph theory, have been successfully used in various types of time series data. In this paper, we review the studies on graph theory to investigate and analyze time series social networks data including different efficient and scalable experimental modalities. We provide some applications, challenging issues and existing methods for time series anomaly detection.

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Index Terms

Computer Science
Information Sciences

Keywords

Social networks Time Series Analysis Anomaly Detection

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