University of Sydney School of Computer Science
COMP5048 Assignment 1: Individual Work
By:
COMP5048 Visual Analytics 2023S1 Assignment 1: Individual work Deadline: April 6 (Week 7) Thursday 23:59pm (pdf on Canvas)
Construct good visualisations of FOUR of the following data to answer the given task.
• You can use any suitable layout chosen from the tools assigned to each category.
• Create visualisations for each data according to the instructions given.
• In your report, explain your justification for your selected visualisation and analysis
methods, then evaluate and compare the pros and cons of your visualisations.
• (Note: different tools not listed can be used for analysis before visualising.)
Data: Choose one graph from each category A, B, C, D:
Category A:
For this category, visualise the data using any tool from the following: Tulip, D3
A1: Best-selling albums of 2000s
• Visualise the whole tree using TWO different layouts.
• Select one label from the data, e.g., could be the most popular label or your favourite, and
extract the subtree rooted at the label.
• Visualise the subtree rooted at the selected label.
A2: Best-selling singles of 2000s
• Visualise the whole tree using TWO different layouts.
• Select one genre from the data, e.g., could be the most popular genre or your favourite, and
extract the subtree rooted at the genre.
• Visualise the subtree rooted at the selected genre.
Category B:
For this category, visualise the data using any tool from the following:
yEd, Tulip, Gephi, NetworkX
B1: Composers graph
• Analyse the graph to identify the top 150 composers and extract the induced subgraph
containing the top composers.
• Visualise the subgraph using TWO different layouts.
• Analyse the subgraph using graph analysis methods to identify the most influential
composers and display the analysis results in the visualisation.
B2: TVCG collaboration graph
• Analyse the graph to identify the top 150 collaborators and extract the induced subgraph
containing the top collaborators.
• Visualise the subgraph using TWO different layouts.
• Analyse the subgraph using graph analysis methods to identify the most influential
collaborators and display the analysis results in the visualisation.
Category C:
For this category, visualise the data using any tool from the following: yEd, Tulip, Graphviz
C1: Movie remakes
• Analyse the graph to identify the influential directors and movies.
• Visualise the graph using TWO different layouts.
• Display the analysis results in the visualisation.
C2: Hrafnkels Saga
• Analyse the graph to identify the influential characters and relationships.
• Visualise the graph using TWO different layouts.
• Display the analysis results in the visualisation.
Category D:
For this category, visualise the data using any tool from the following: yEd, Tulip, Gephi, NetworkX, D3
D1: “INFECTIOUS” exhibition interaction network
• Analyse the graph to identify the important people and interactions, both in each time slice
and over all time slices.
• Visualise the graph, showing all time slices of the graph, using TWO different methods.
• Display the analysis results in the visualisation.
D2: Workplace contacts graph
• Analyse the graph to identify the important people and interactions, both in each time slice
and over all time slices.
• Visualise the graph, showing all time slices of the graph, using TWO different methods.
• Display the analysis results in the visualisation.
Submission: Minimum 16 - page report Minimum 4 pages per data:
• 1st page: 1st Visualisation
• 2nd page: 2nd Visualisation
• 3rd-4th pages: Description with the following subheadings:
◦ Design: tools and layouts with justification (design choice)
◦ Analysis: explain analysis methods used with justification on how they support the task
◦ Evaluation: comparison of pros and cons between the two visualisations
◦ Acknowledge all your sources in References
◦ Provide code, if applicable, in Appendix
◦ In Appendix, you can include one more visualisation for each data:
▪ Should be substantially different using different techniques
▪ Include description as above for each alternative visualisation
▪ Add comparison between different visualisations of the same data
Marking Rubric: (5 marks per data; total 20 marks)
• Quality of visualisation: 3 marks
• Quality of analysis: 1 mark
• Quality of evaluation: 1 mark
