Lab work
We will conduct our lab work using the open source software R. You may find this cheat sheet useful.
To learn advanced programming techniques in R and its functional nature, see Hadley Wickman, Advanced R (electronic book), and the same author's paper on The Split-Apply-Combine strategy for data analysis (the plyr package). Another useful source of tips for good R programming practices: P. Burns, The R Inferno.
- LAB 1 (13-Sep): Introduction to igraph [guide] [data]
- Deadline for deliverable is Sept. 27, 23:00, please submit your solution via the Raco
- R as functional language: a script to show advantages of functional R versus procedural R. [R_functional_Proc]
- LAB 2 (21-Oct): Analysis of the degree distribution [files]
- Deadline for deliverable is Oct. 4th, please submit your solution via the Raco
- LAB 3 (5-Oct): Significance of network metrics [files]
- Deadline for deliverable is Oct. 18th, please submit your solution via the Raco
- LAB 4 (20-Oct): Non-linear regression on dependency trees [guide]
- Deadline for deliverable is Nov. 8th, please submit your solution via the Raco
- LAB 5 (9-Nov): Community finding algorithms [guide].
Datasets of networks repositories:
The Stanford Large Network Dataset Collection
Datasets for Social Network Analysis |AMiner - Deadline for deliverable is Nov. 22nd, please submit your solution via the Raco
- LAB 6 (23-Nov): Network dynamics [guide]
- Deadline for deliverable is Dec. 6th, please submit your solution via the Raco
- LAB 7 (14-Dec): Epidemic spreading over networks [guide]
- Deadline for deliverable is Dec. 29, please submit your solution via the Raco
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