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Dear Socnetters,

Consider submitting your relevant work to the 'Statistical network modelling’ session of the conference ‘Networks in the Global World’ (St. Petersburg, July 7-9).

Deadline: January 13, 2020.

Invited speaker and chair: Peng Wang, Swinburne University of Technology

Invited speaker: Christoph Stadtfeld, ETH Zürich.


Traditional network metrics describe parameters of observed networks. Meanwhile, understanding of the processes that influenced the formation of observed network structure requires statistical models that represent distributions of networks with similar structural features as the observed network, hence inferring local network processes based on estimated parameter values. By considering the interdependent nature of network links and the properties of the involved nodes, current statistical modelling techniques allow to account for different network configurations, as well as for nodal and dyadic level attributes, in order to determine the sets of factors that have strong influence on the formation of an observed network. There are extensions for longitudinal data revealing how the interplay of those factors unfolds in time.

Moreover, the recent model developments for multi-partite networks comprised of links between ontologically different nodes, multiple networks consisting of multiple types of links among the same set of nodes, as well as multilevel networks combining multi-partite and unipartite networks, allows inferences on how these networks affect one another by simultaneously account for relations within and between networks of different kinds – inter-personal networks, semantic networks, organizational networks, material objects networks, networks of spaces, etc. For example, one can model how the usage of concepts connected in a semantic network is related to the existence of inter-personal ties. The models can also be compared to find differences and similarities in the formation of networks in different cultures, societies, states, economies, organizations, cities, etc.

This session invites papers discussing methodological issues in statistical network modelling. Particularly encouraged are multi-partite, multilevel and multiple models and hypotheses driven by developments in multimode network theory and applications along with the existing hypotheses tested on new data.

Presentations of developments in relevant software would also be appreciated.

Find a full description of the session here: http://ngw.spbu.ru/programme.


The Fifth Biannual International Conference ‘Networks in the Global World’ will be held with support from International Network for Social Network Analysis (INSNA), International Sociological Association (ISA), and German Academic Exchange Service (DAAD) at St Petersburg University.

Conference call for abstracts is available here: https://zdes.spbu.ru/images/Call_for_abstracts_NetGloW_2020.pdf.

Authors of selected abstracts will be invited to submit full papers before June 1, 2020 to be published in the ‘NetGloW 2020’ volume of the Springer’s ‘Lecture Notes in Networks and Systems’ indexed in Scopus: https://www.springer.com/series/15179.

Note that citizens of most of the European countries can get an electronic Visa to St. Petersburg for up to 8 days online, see http://ngw.spbu.ru/practical.

Find out more about NetGloW’20 on the official website of the conference: http://ngw.spbu.ru

Best wishes for 2020,


Dr. Nikita Basov
Senior researcher in Sociology, St. Petersburg University
Scientific Manager, Centre for German and European Studies, St. Petersburg University - Bielefeld University
+7 812 324 08 85

Recent papers:

2019 Basov, N., De Nooy, W., Nenko, A. Local Meaning Structures: A Socio-Semantic Network Analysis of Artistic Collectives. American Journal of Cultural Sociology: https://link.springer.com/article/10.1057/s41290-019-00084-9

2019 Basov, N. The Ambivalence of Cultural Homophily: Field Positions, Semantic Similarities, and Social Network Ties in Creative Collectives. Poetics: https://www.sciencedirect.com/science/article/abs/pii/S0304422X18302808?via%3Dihub.
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