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B A E H Figure 1 K Questions 1. List the shortest paths between all node pairs. Indicate the number of shortest paths that pass through each edge. Explain how this information helps determine edge betweenness. 2. Compute the edge betweenness for each configuration of DFS. 3. Remove the edge(s) with the highest betweenness and redraw the graph. Recompute the edge betweenness centrality for the new graph. Explain how the network structure changes after removing the edge. 4. Iteratively remove edges until at least two communities form. Provide step-by-step calculations for each removal. Explain how edge betweenness changes dynamically during the process. 5. How many communities do you detect in the final step? Compare the detected communities with the original graph structure. Discuss whether the Girvan- Newman algorithm successfully captures meaningful subgroups. 6. If you were to use degree centrality instead of edge betweenness for community detection, how would the results change?

B A E H Figure 1 K Questions 1. List the shortest paths between all node pairs. Indicate the number of shortest paths that pass through each edge. Explain how this information helps determine edge betweenness. 2. Compute the edge betweenness for each configuration of DFS. 3. Remove the edge(s) with the highest betweenness and redraw the graph. Recompute the edge betweenness centrality for the new graph. Explain how the network structure changes after removing the edge. 4. Iteratively remove edges until at least two communities form. Provide step-by-step calculations for each removal. Explain how edge betweenness changes dynamically during the process. 5. How many communities do you detect in the final step? Compare the detected communities with the original graph structure. Discuss whether the Girvan- Newman algorithm successfully captures meaningful subgroups. 6. If you were to use degree centrality instead of edge betweenness for community detection, how would the results change?

Fundamentals of Information Systems
Fundamentals of Information Systems
8th Edition
ISBN:9781305082168
Author:Ralph Stair, George Reynolds
Publisher:Ralph Stair, George Reynolds
Chapter3: Database Systems And Applications
Section: Chapter Questions
Problem 2.1DQ
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B A E H Figure 1 K Questions 1. List the shortest paths between all node pairs. Indicate the number of shortest paths that pass through each edge. Explain how this information helps determine edge betweenness. 2. Compute the edge betweenness for each configuration of DFS. 3. Remove the edge(s) with the highest betweenness and redraw the graph. Recompute the edge betweenness centrality for the new graph. Explain how the network structure changes after removing the edge. 4. Iteratively remove edges until at least two communities form. Provide step-by-step calculations for each removal. Explain how edge betweenness changes dynamically during the process. 5. How many communities do you detect in the final step? Compare the detected communities with the original graph structure. Discuss whether the Girvan- Newman algorithm successfully captures meaningful subgroups. 6. If you were to use degree centrality instead of edge betweenness for community detection, how would the results change?
Transcribed Image Text:B A E H Figure 1 K Questions 1. List the shortest paths between all node pairs. Indicate the number of shortest paths that pass through each edge. Explain how this information helps determine edge betweenness. 2. Compute the edge betweenness for each configuration of DFS. 3. Remove the edge(s) with the highest betweenness and redraw the graph. Recompute the edge betweenness centrality for the new graph. Explain how the network structure changes after removing the edge. 4. Iteratively remove edges until at least two communities form. Provide step-by-step calculations for each removal. Explain how edge betweenness changes dynamically during the process. 5. How many communities do you detect in the final step? Compare the detected communities with the original graph structure. Discuss whether the Girvan- Newman algorithm successfully captures meaningful subgroups. 6. If you were to use degree centrality instead of edge betweenness for community detection, how would the results change?
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