BUSM132: Complex Networks and Innovation - Research Project - Business Assignment Help

Assignment Task:

Students must choose one of the following titles: 

1. Critically assess, compare and contrast the random Poisson network model and the “scale-free” network model proposed by Barabási and Albert. With the aid of examples, discuss whether and the extent to which these models offer an accurate representation of real-world networks. 

2. In the social sciences, scholars seem to agree on the importance of network structure, but disagree on the relative benefits of opposite types of structures. Some regard closed network structures (cohesion) as the source of social capital and innovation. Others have argued in favour of the benefits of open network structures (brokerage). With the aid of examples, critically assess, compare and contrast the network mechanisms underpinning these (apparently) opposing views of social capital. 

3. Critically outline and assess the following network measures: a) the clustering coefficient; b) the average geodesic distance; and c) the average degree. By drawing on relevant network theories, conduct a critical and comparative evaluation of the role played by each of the above measures in the following network models: a) the fully connected network; b) the Poisson random network; c) the Barabási-Albert “scale-free” network; and d) the Watts- Strogatz “small-world” network. 

4. The experiment that Stanley Milgram conducted in the 1960s provided empirical evidence in favour of what is now referred to as the “small-world effect”, namely the fact that the average geodesic distance is small even for very large networks. Critically discuss Milgram’s experiment, and assess the extent to which the “small-world effect” can be found in the random Poisson 

network, in the Barabási-Albert “scale-free” network, and in the Watts- Strogatz “small-world” network. 

5. By drawing on the Barabási-Albert “scale-free” network and the Watts- Strogatz “small-world” network, critically assess the implications of different network structures and properties (e.g., clustering, average distances) for real- world processes such as, for example, information and innovation diffusion, opinion dynamics, and viral marketing. 

Learning Objectives: 

The objective of the project is to allow students to develop the ability of undertaking independent work, while applying and extending ideas and methods introduced in the course. 

Learning Outcomes: 

Depending on the selected subject and approach, students should be able to: 

• start up or redirect a line of inquiry; 

• draw upon multiple disciplines; 

• employ diverse methods, including qualitative, field, survey, archival, laboratory and computational methods; 

• focus on different kinds of networks including acquaintance networks, co- authorship networks, inter- and intra-organizational networks; 

• make a theoretical contribution; 

• provide new empirical findings; 

• achieve genuine integration of theory and data; 

• provide a theoretically-driven review and integration of an important research area. 

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