scholarly journals Evaluating user cognition of network diagrams

2021 ◽  
Author(s):  
Xiaojiao Chen ◽  
Xiaoteng Tang ◽  
Zijing Luo ◽  
Jiayi Zhang
Keyword(s):  
Network Diagrams

Big Neuroscience

2015 ◽  
Vol 1 (3) ◽  
Author(s):  
Erik Larson
Keyword(s):  
Big Data ◽  
Brain Regions ◽  
Network Diagrams

Modern neuroscience depends on gigantic computer-generated models and network diagrams of neurons, neuron circuits, and brain regions. If Big Data can offer new insights, this field might be the place to look. Or not. Erik Larson criticizes claims about Big Data.

Analyzing Work Teams Using Social Network Diagrams

2014 ◽  
pp. 170-191
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Network ◽  
Social Dynamics ◽  
Power Structure ◽  
Work Team ◽  
Team Members ◽  
The Social ◽  
Workplace Teams ◽  
Project Phases ◽  
Shed Light ◽  
Network Diagrams

Workplace teams are a common social structure that enables the successful completion of collaborative projects. They have been studied as “hot” teams, virtual ones, and other manifestations. For both management and team members, it is helpful to have a form of meta-cognition on teams to solve work team issues pre-, during-, and post-project. One way to systematize understandings of a work team is to apply social network analysis to depict the work team’s power structure, its functions, and ways to improve the team’s communications for productivity, creativity, and effective functioning. This chapter depicts three real-world team-based projects as social network diagrams along with some light analysis. This work finds that social network diagrams may effectively shed light on the social dynamics of projects in the pre-, during-, and post-project phases.

Extending Social Network Diagrams for Large Scale Collaboration

2012 ◽  
pp. 1-13
Author(s):  
I.T. Hawryszkiewycz
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Web 2.0 ◽  
Large Scale ◽  
Policy Planning ◽  
Web 2.0 Technologies ◽  
Enterprise Social Networks ◽  
Cloud Technologies ◽  
Common Vision ◽  
Network Diagrams

The chapter provides a way for modeling large scale collaboration using an extension to social network diagrams called enterprise social networks (ESNs). The chapter uses the ESN diagrams to describe activities in policy planning and uses these to define the services to be provided by cloud technologies to support large scale collaboration. This chapter describes collaboration by an architecture made up of communities each with a role to ensure that collaboration is sustainable. The architecture is based on the idea of an ensemble of communities all working to a common vision supported by services provided by the collaboration cloud using Web 2.0 technologies.

Application of #Chain Equations to Resource Scheduling

2013 ◽  
Vol 824 ◽  
pp. 544-552
Author(s):  
U.J. Udosen
Keyword(s):  
Resource Availability ◽  
Resource Constraints ◽  
Resource Scheduling ◽  
The Other ◽  
Project Duration ◽  
Network Diagrams

Chain (pronounced number chain) equations were applied in the resource scheduling of two projects. The first project involved the scheduling of one type of manpower with unlimited resource availability while the second involved categorized manpower with resource constraints. The Earliest Start (ES), Latest Start (LS) and Leveling methods were applied to schedule the first project and were characterized by plotting network diagrams to schedule the project. When the #Chain approach was employed to schedule the same project, plotting of network diagrams was obviated but yielded similar manpower profiles and project duration of 12 weeks as the other methods. The ES, LS and Leveling methods did not lend themselves to scheduling of the project with categorized manpower having constraints and were not applied for scheduling the second project. However, the #Chain equations were applied, with ease, to schedule the second project to generate manpower profiles for each type of manpower. Due to the constraints applied, the project duration was extended from 12 weeks to 14 weeks. Hence the #Chain approach proffers a simple and preferred methodology for scheduling categorized resources with their attendant constraints.

scholarly journals A Network Analysis of the Asia-Pacific and Other Developed Stock Markets: Pre and Post Global Financial Crisis

2020 ◽  
Vol 9 ◽  
pp. 112-131
Author(s):  
Kousik Guhathakurtha ◽  
Sharad Nath Bhattacharya ◽  
Mousumi Bhattacharya
Keyword(s):  
Hong Kong ◽  
Financial Crisis ◽  
Portfolio Management ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Forecast Error ◽  
Asia Pacific ◽  
Volatility Spillover ◽  
Risk Transfer ◽  
Network Diagrams

This paper examines the volatility spillover and connectedness between Asia-Pacific, US, UK, and eurozone stock markets. A spillover index is built using forecast error variance decomposition in a vector autoregression framework and the spillover index is used to build network diagrams. It shows evidence of how the increase in risk transfer (volatility spillover) between the markets led to the global financial crisis and of the higher level of connectedness since. Network diagrams show the direction and strength of the connectedness. The network strength estimation enables us to understand the risk associated with connectedness across the markets in the event of a trigger and its influence in portfolio management decisions of international funds. The Chinese market appears to be the most insulated, while the South Korean, Hong Kong, and Singapore stock markets dominate in terms of risk transfer. The US, UK, EU, Singapore and Hong Kong are the top five volatility spillover recipient markets, both during pre and post global financial crisis periods. We find the market size to be irrelevant in the determination of the level of connectedness, whereas the role of geographical proximity cannot be ruled out. The findings are relevant to multinational investment strategies and in understanding the relative risk of investment in the Asia-Pacific region.

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