About

Arthur Pecherskikh (apecherskikh@eu.spb.ru | aapecherskikh@hse.ru)
last update: 2025-12-01

The network metaphor has become a prevalent part of contemporary societies. Ideas that have been developed in the social sciences over decades are now widely accepted by the general public. We frequently discuss the importance of “networking”, someone’s “connections”, or “social capital”. The daily presence of social media platforms (e.g., Telegram, VK, Twitter) in our lives – where we observe others’ numbers of friends and are often surprised when two seemingly distant acquaintances turn out to be connected – makes the subject of this course self-evident on the surface. However, the social network analysis (SNA) is much more than just a common metaphor; it is a powerful and well-formalized set of methods used to study complex real-world phenomena, from the spread of rumors or diseases (as seen recently with COVID-19) to global trade networks and the rise and fall of creativity in cultural industries.

SNA is an analysis-heavy subfield, meaning (1) it is challenging to come up with a general theory of social networks due to their diversity and complexity (viewing social reality relationally is one of the few unifying principles), and (2) it is difficult to grasp without hands-on experience with data. For these reasons, this course will focus on teaching you (1) how to develop researchable ideas and (2) how to test these ideas using available statistical software. The general goal of this course is thus to provide participants with essential insights into social networks and to equip them with the skills to analyze networks computationally.

About me

My name is Arthur Pecherskikh, I am a second-year PhD student at European University at St. Petersburg (sociology program) and researcher at the Center for Institutional Analysis of Science & Education. I am also a visiting lecturer at the Higher School of Economics (St. Petersburg). My research interests include social network analysis, sociology of science, artistic consecration, sociology of sociology, and computational text analysis.

This is the second time I teach networks at the master program “Data Analytics for Business and Economics” (Higher School of Economics, St. Petersburg). The last-year course page at HSE website is available here. The organization and structure of this course have been heavily influenced by Maria Safonova eponymous lectures I took some years ago. In developing the course, I also consulted the syllabi created by Nadya Sokolova (2023, delivered at Eurpean University at St. Petersburg), John Levi Martin (2008), Paul McLean (2021), Mark Mizruchi (2008), Chris Smith (2022), and Steve Borgatti (2004).

Whatever question about the course you have, please, contact me via corporate email (aapecherskikh@hse.ru) or telegram (my nickname is @archibard).