Dynamic social network analysis: A novel approach using agent‐based model, author‐topic model, and pretopology

2019 ◽  
Vol 32 (13) ◽  
Author(s):  
Thi Kim Thoa Ho ◽  
Quang Vu Bui ◽  
Marc Bui
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Topic Model ◽  
Agent Based Model ◽  
Agent Based ◽  
Dynamic Social Network ◽  
Novel Approach ◽  
Model Author

scholarly journals Combining social network analysis and agent-based model for enabling nature-based solution implementation: The case of Medina del Campo (Spain)

2021 ◽  
pp. 149734
Author(s):  
R. Giordano ◽  
M. Manez-Costa ◽  
A. Pagano ◽  
B. Mayor Rodriguez ◽  
P. Zorrilla-Miras ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Agent Based Model ◽  
Agent Based

Aspects of Various Community Detection Algorithms in Social Network Analysis

2019 ◽  
pp. 327-341
Author(s):  
Nicole Belinda Dillen ◽  
Aruna Chakraborty
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Novel Approach ◽  
Recent Developments ◽  
Overlapping Community ◽  
Related Individuals

One of the most important aspects of social network analysis is community detection, which is used to categorize related individuals in a social network into groups or communities. The approach is quite similar to graph partitioning, and in fact, most detection algorithms rely on concepts from graph theory and sociology. The aim of this chapter is to aid a novice in the field of community detection by providing a wider perspective on some of the different detection algorithms available, including the more recent developments in this field. Five popular algorithms have been studied and explained, and a recent novel approach that was proposed by the authors has also been included. The chapter concludes by highlighting areas suitable for further research, specifically targeting overlapping community detection algorithms.

scholarly journals Explaining the Development of Information Security Climate and an Information Security Support Network: A Longitudinal Social Network Analysis

2019 ◽  
Vol 23 ◽  
Author(s):  
Duy Dang-Pham ◽  
Karlheinz Kautz ◽  
Siddhi Pittayachawan ◽  
Vince Bruno
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Security ◽  
Support Network ◽  
Novel Approach ◽  
Individual Cognition ◽  
Climate Perceptions ◽  
Personal Advice ◽  
Practical Recommendations

Behavioural information security (InfoSec) research has studied InfoSec at workplaces through the employees’ perceptions of InfoSec climate, which is determined by observable InfoSec practices performed by their colleagues and direct supervisors. Prior studies have identified the antecedents of a positive InfoSec climate, in particular socialisation through the employees’ discussions of InfoSec-related matters to explain the formation of InfoSec climate based on the employees’ individual cognition. We conceptualise six forms of socialisation as six networks, which comprise employees’ provisions of (1) work advice, (2) organisational updates, (3) personal advice, (4) trust for expertise, (5) InfoSec advice, and (6) InfoSec troubleshooting support. The adoption of a longitudinal social network analysis (SNA), called stochastic actor-oriented modelling (SAOM), enabled us to analyse the changes in the socialising patterns and the InfoSec climate perceptions over time. Consequently, this analysis explains the forming mechanisms of the employees’ InfoSec climate perceptions as well as their socialising process in greater detail. Our findings in relation to the forming mechanisms of InfoSec-related socialisation and InfoSec climate, provide practical recommendations to improve organisational InfoSec. This includes identifying influential employees to diffuse InfoSec knowledge within a workplace. Additionally, this research proposes a novel approach for InfoSec behavioural research through the adoption of SNA methods to study InfoSec-related phenomena.

PRIOR KNOWLEDGE VERSUS CONSTRUCTED KNOWLEDGE: WHAT IMPACT ON LEARNING?

2008 ◽  
Vol 11 (02) ◽  
pp. 289-302 ◽  
Author(s):  
WIDAD GUECHTOULI
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Prior Knowledge ◽  
Learning Process ◽  
Agent Based Model ◽  
Agent Based ◽  
Congestion Effect ◽  
Over Time

The aim of this paper is to model the process of learning within a social network and compare the levels of learning in two different situations: one where individuals know others' competencies as given data and interact on this basis; and one where individuals know nothing about others' competencies but rather build this knowledge over time, according to their past interactions. For this purpose, we build an agent-based model, and model these two scenarios of simulations. Results are partly studied using network analysis, and they show that in the second type of simulations agents are able to identify the most competent agents in the network and increase their competencies. Results also show that learning is easier when there is no prior knowledge of others' competencies. Otherwise, agents deal with a congestion effect that slows down the learning process.

Dynamic Social Network Mining

2013 ◽  
pp. 122-144
Author(s):  
Luca Cagliero ◽  
Alessandro Fiori
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Knowledge Discovery ◽  
Network Mining ◽  
Social Network Mining ◽  
Dynamic Social Network ◽  
Main Work ◽  
Insight Into

This Chapter overviews most recent data mining approaches proposed in the context of social network analysis. In particular, it aims at classifying the proposed approaches based on both the adopted mining strategies and their suitability for supporting knowledge discovery in a dynamic context. To provide a thorough insight into the proposed approaches, main work issues and prospects in dynamic social network analysis are also outlined.

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