Emphasizing on Space Complexity in Enterprise Social Networks for the Investigation of Link Prediction Using Hybrid Approach

2020 ◽  
pp. 253-270
Author(s):  
J. Gowri Thangam ◽  
A. Sankar
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
Hybrid Approach ◽  
Space Complexity ◽  
Enterprise Social Networks

Analysing Asymmetrical Associations using Fuzzy Graph and Discovering Hidden Connections in Facebook

2017 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Amita Jain ◽  
Sunny Rai ◽  
Ankita Manaktala ◽  
Lokender Sarna
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
Hybrid Approach ◽  
Absolute Error ◽  
Fuzzy Graph ◽  
Relationship Strength ◽  
Relational Networks ◽  
Actual Outcome ◽  
The Relationship ◽  
Transactional Data

The fuzzy graph theory to analyse the relationship strength in Social Networks has gain significant potential in last few years and has seen applications in areas like Link Prediction, calculating Reciprocity, discovering central nodes etc. In this paper, we propose a framework to analyse and quantify the degree of strength of asymmetric relationships and predict hidden links in social networks using fuzzy logic. Till now, the work in fuzzy social relational networks has been limited to symmetric relationships. However, in this paper, we consider the scenario of asymmetric relations. The proposed approach is for web 2.0 application <em>Facebook</em>. Our contribution is three fold. First, the measurement of the strength of asymmetric relationship between nodes on the basis of social interaction using the concept of fuzzy graph. Second, a hybrid approach for prediction of missing links between two nodes on the basis of similarity of attributes of user profiles such as demographic, topology and network transactional data. Third, we perform fuzzy granular computing on attribute ‘strength of relationship’ and categorise into four granules namely <em>{socially close friends, socially near friends, socially far friends, socially very far friends}</em> based on the results of supervised learning conducted over dataset. Similarly, actual outcome for predicted links is categorised into three granules namely <em>Accept, Not accept and May be.</em> The proposed approach has predicted relationship strength with mean absolute error of 9.26% whereas the proposed approach for Link prediction has provided 64% correct predictions.

scholarly journals Investigating the Impact of Critical Factors on Continuous Usage Intention towards Enterprise Social Networks: An Integrated Model of IS Success and TTF

2021 ◽  
Vol 13 (14) ◽  
pp. 7619
Author(s):  
Run-Ze Wu ◽  
Xiu-Fu Tian
Keyword(s):  
Social Networks ◽  
Structural Equation ◽  
Equation Modeling ◽  
Critical Factors ◽  
Information Systems Success ◽  
Is Success ◽  
Enterprise Social Networks ◽  
Task Technology Fit ◽  
Continuous Usage Intention ◽  
The Impact

Due to the outbreak of COVID-19, many people have to accept remote working. However, as COVID-19 has been effectively controlled in China, remote office services provided by enterprise social networks (ESNs) is no longer a necessary choice of users. There has not yet been any referential research for ESN enterprises concerning how to encourage users willing to use ESNs continuously. Therefore, the purpose of this research is to identify the critical factors of ESN continuous usage intention to make up the research gap of ESN continuous usage intention and to help enterprises address the issue of sustained growth. This research combines elements of the task technology fit (TTF) model and D&M information systems success (ISS) model, explaining the continuous usage intention of ESN users. The empirical analysis results are based on the sample data of 668 Chinese respondents with experience in ESNs use and analyzed using structural equation modeling (SEM). Results show that task technology fit, performance expectancy and the satisfaction degree have a significant influence on continuous usage intention of ESNs. The research findings can provide the theoretical basis for sustained development and follow-up research of the ESN industry.

Combining contextual, temporal and topological information for unsupervised link prediction in social networks

2018 ◽  
Vol 156 ◽  
pp. 129-137 ◽  
Author(s):  
Carlos Pedro Muniz ◽  
Ronaldo Goldschmidt ◽  
Ricardo Choren
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
Topological Information

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.

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