A deep learning approach for semi-supervised community detection in Online Social Networks

2021 ◽  
pp. 107345
Author(s):  
Aniello De Santo ◽  
Antonio Galli ◽  
Vincenzo Moscato ◽  
Giancarlo Sperlì
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Community Detection ◽  
Online Social Networks ◽  
Learning Approach

scholarly journals DeepOSN: Bringing deep learning as malicious detection scheme in online social network

2020 ◽  
Vol 9 (1) ◽  
pp. 146
Author(s):  
Putra Wanda ◽  
Marselina Endah Hiswati ◽  
Huang J. Jie
Keyword(s):  
Social Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Online Social Networks ◽  
Link Prediction ◽  
Online Social Network ◽  
Detection Scheme ◽  
Malicious Activity ◽  
Manual Analysis ◽  
Learning Techniques

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.

Deep learning-based personality recognition from text posts of online social networks

2018 ◽  
Vol 48 (11) ◽  
pp. 4232-4246 ◽  
Author(s):  
Di Xue ◽  
Lifa Wu ◽  
Zheng Hong ◽  
Shize Guo ◽  
Liang Gao ◽  
...  
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Online Social Networks ◽  
Personality Recognition

Community Detection in Online Social Networks: A Differentially Private and Parsimonious Approach

2020 ◽  
Vol 7 (1) ◽  
pp. 151-163 ◽  
Author(s):  
Tianxi Ji ◽  
Changqing Luo ◽  
Yifan Guo ◽  
Qianlong Wang ◽  
Lixing Yu ◽  
...  
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Online Social Networks

scholarly journals Deep Learning Techniques for Community Detection in Social Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 96016-96026 ◽  
Author(s):  
Ling Wu ◽  
Qishan Zhang ◽  
Chi-Hua Chen ◽  
Kun Guo ◽  
Deqin Wang
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Community Detection ◽  
Learning Techniques

Community detection in social networks

2017 ◽  
Vol 01 (01) ◽  
pp. 1630001 ◽  
Author(s):  
Hossein Fani ◽  
Ebrahim Bagheri
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Computer Science ◽  
Community Detection ◽  
Online Social Networks ◽  
Online Community ◽  
Detection Methods ◽  
Fundamental Part ◽  
Social Settings ◽  
Online Experience

Online social networks have become a fundamental part of the global online experience. They facilitate different modes of communication and social interactions, enabling individuals to play social roles that they regularly undertake in real social settings. In spite of the heterogeneity of the users and interactions, these networks exhibit common properties. For instance, individuals tend to associate with others who share similar interests, a tendency often known as homophily, leading to the formation of communities. This entry aims to provide an overview of the definitions for an online community and review different community detection methods in social networks. Finding communities are beneficial since they provide summarization of network structure, highlighting the main properties of the network. Moreover, it has applications in sociology, biology, marketing and computer science which help scientists identify and extract actionable insight.

scholarly journals Followers Are Not Enough: A Multifaceted Approach to Community Detection in Online Social Networks

PLoS ONE ◽  
2015 ◽  
Vol 10 (8) ◽  
pp. e0134860 ◽  
Author(s):  
David Darmon ◽  
Elisa Omodei ◽  
Joshua Garland
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Online Social Networks ◽  
Multifaceted Approach
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