Summary

This workshop is primarily aimed at Python programmers, either academics, professionals or students, that wish to learn the basics of modern network science and practical analyses of real networks, such as social, information and biological networks. Familiarity with the basics of probability theory and statistics, linear algebra, and machine learning is strongly encouraged.

The workshop is based on the Masters level course Network analysis offered by the instructor.

Syllabus

From graph theory to network science.
Structure of networks and graph models.
Node importance and link analysis algorithms.
Community, core-periphery and other structures.
Network visualization, mining and applications.
Introduction to machine learning on graphs.

Instructors

Lovro Šubelj is a researcher and lecturer in the field of network science and data analysis. He is an assistant professor at the University of Ljubljana, and has almost 15 years of professional and academic experience in network analysis. He is a coauthor of more than 60 scientific papers on network science, and has developed original network analysis methods and algorithms for companies such as Petrol and Celtra. Together with his fellow colleagues he is also organizing the only network analysis conference in Slovenia called NetSlo.

Attendee equipment prerequisites

It is recommended that you bring a laptop with a working installation of Python, and NetworkX, CDlib and node2vec packages. Alternatively, you can work with any other network analysis package, such as igraph, graph-tool or SNAP.py. For visualization of smaller networks, it can be useful to have an installation of some network analysis software, such as Gephi or visone.

Access to all the materials, code, and datasets will be provided to participants a few days before the workshop.

In order to attend the workshop the vaccinated-tested-recovered rule (PCT) must be met and you need to show the certificate to the security guard when you enter the building.

Introduction to network science in Python (NetPy)

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