Week: Lecture calendar | Advanced Laboratory of Complex Network Analysis 2027 | INF - e-learning

Section outline

Being a Laboratory course, lectures will take place in presence for MS students.
PhD candidates are allowed to join remotely to through the MS Teams class.

For each lecture are reported the book chapters/papers to be studied: while some of them are mandatory (i.e., most book chapters and surveys) others are suggested readings.

T: Theory lectures
L: Laboratory lectures
G: Group project labs


	Type	Topic	Slides	Materials
1	T	Intro & Network Science Recap	Slides	📕 Ch.2 📙 Ch.3
2	T	Where to start: Formulating Hypotheses	Slides	📗 Ch. 2-3
3	G	Group Project: group formation and available datasets	Slides
4	T	Modeling Choices: From simple graphs to advanced models	Slides	📙 Ch. 4
5	L	Network Modeling: Static & Dynamic Networks	Notebooks 1, 2, 3, 4
6	L	Network Modeling: Multiplex Networks	Notebook
7	L	Network Modeling: Higher-order structures	Notebook
8	T	Enriching Topologies: Feature-rich modeling	Slides
9	L	Graph Augmentation: Node/Edge attributes	Notebook
10	T	Network Sampling	Slides	📙 Ch. 25
11	L	Network sampling	Notebook
12	G	Group project
13	T	Data Collection: API, Web Scraping & Generation	Slides	YSocial
14	L	Data Collection: API, Web Scraping & Generation	Notebook
15	G	Group project
16	T	Graph Transformation	Slides	📙 Ch. 24
17	L	Graph Transformation	Notebook
18	G	Group project
19	G	Group project
20	T	How to Validate: check statistical significance of network-based studies	Slides	📙 Ch. 16 📗 Ch. 4-5
21	L	Null model and statistical significance testing
22	G	Group project
23	T/L	Experiment reproducibility & Open Science	Slides
24	T	Experiments from A to Z: Examples and Case Study