Tag: GDPR

Describe briefly your network. What/who are the nodes? What do the edges represent? What type of a network is it (i.e. directed, undirected, …)? How did you collect the network data (i.e. is it from memory, if it is based on secondary data how did you collect these data …).

present and study a network that you will create yourself.

The network should have at least 10 nodes. There is no upper limit on the number of nodes.

The network should not be fully connected (that is, not all nodes should have ties with all other nodes). Otherwise, it will be a rather trivial network to study.

You should not include any personal data in presenting and analysing the network (that is, use pseudonyms or anonymous IDs for the nodes in the network, we don’t want to violate GDPR). If the network you study is freely available in the public domain (celebrities, fictional characters, sportspeople, firms, fictional characters…) you may use real names.

Any type of network discussed in class is allowed (directed, undirected, weighted, unweighted, bipartite, one-mode, signed, unsigned …)

Nodes in the network could be of any type (people, organisations, companies, book characters, …)

The network should be original, that is, it should not be a network studied previously by other scholars, or a network data of which is readily available.

Based on your network write a short (1,500 words) report. Your report should discuss at a minimum the items given below. You may comment on additional properties of your network once you cover all items below. You may write a single report discussing all items. You may also structure your report in four parts corresponding to the four groups of items below. Note that the interpretation of a particular network measure is as important as correctly calculating and reporting the measure. So, make sure to include an interpretation of the network measures you report.

A: Description of your network:

Describe briefly your network. What/who are the nodes? What do the edges represent? What type of a network is it (i.e. directed, undirected, …)? How did you collect the network data (i.e. is it from memory, if it is based on secondary data how did you collect these data …).

B: Characteristics of the network and the nodes:

What is the density and diameter in your network? Apply at least three measures of centrality to study the importance of the nodes in your network. Report the values of these centrality scores for the most central four or five nodes. Interpret these centrality measures. Based on these centrality scores who are the most important two or three nodes in your network and why? Comment on how centralized your network is.

C: Characteristics of groups of nodes:

Does your network have any cliques? Describe the k-cores of your network. Are there any structurally equivalent nodes in your network? Run a formal blockmodeling, comment on any nodes that look structurally equivalent to you and interpret the results of your blockmodeling.

D: Characteristics of the edges:

Study the transitivity of the network by reporting and interpreting the global and local clustering coefficients. If it is a directed network, also calculate and interpret the reciprocity of the network. If it is a signed network, comment on whether your network is structurally balanced.

What are the general requirements and major impacts of the GDPR, and how does this law differ from data protection legislation in the United States?

Discussion

The European Union legislation called the “General Data Protection Regulation” (GDPR) provides data privacy protections for consumers and has had a major impact on companies around the world.
In your initial post, address the following as part of your response:

• What are the general requirements and major impacts of the GDPR, and how does this law differ from data protection legislation in the United States?

• How are companies outside of Europe affected by the GDPR?

• How do perspectives on the GDPR differ between consumers and businesses? Do perspectives vary by industry?

• Do you think there should be one data privacy law for the entire world? Balance your discussion by weighing the benefits to international companies with your knowledge of the differences in culture, politics, and government around the world and the importance of sovereignty of countries.

In your responses to two or more of your peers, use the following questions to guide your responses:

• Consider how the principles of globalization can be applied to the harmonization of data privacy laws. Do you expect legislation similar to the GDPR to be passed by other countries around the world? Why or why not?

• Do you agree or disagree with your peer’s opinion on whether there should be global data privacy legislation? Why or why not?

• How could your response to the harmonization of data privacy laws apply to other regulatory areas (such as accounting)