Snowball sampling and conditional estimation for exponential random graph models for large networks in high performance computing

Snowball sampling and conditional estimation for exponential random graph models for large networks in high performance computing

Research area
Internal groups
High Performance Methods for Numerical Simulation in Science, Medicine and Engineering
Description

Modeling of social networks is used in sociology, economics, social psychology, public health, management, politics etc. As information and communication technologies continue to expand, the need arises to develop analytical strategies capable of accommodating new and larger sets of social network data. Currently most popular models that describe different kind of social interactions are Exponential Random Graph Models (ERGMs). The main objective of the project is to develop and co-design a new evaluation framework that extends the possibility of specifying and estimating ERGMs for the analysis of very large social networks. This is done by combining new algorithmic ideas with state of the art knowledge in software development for high-performance computing. The project is carried out in collaboration between the Universit√† della Svizzera italiana (USI) and the University of Melbourne, Australia.

The project is funded by Swiss National Platform of Advanced Scientific Computing (PASC).

 SN1SN2
Leaders

Professor Alessandro Lomi; ; PI; ICS Institute of Computational Science

Researcher Dr. Maksym Byshkin; Co-PIs; ICS Institute of Computational Science

Professor Antonietta Mira; Co-PIs; ICS Institute of Computational Science

Professor Garry Robins; Co-PIs; University of Melbourne

Researcher Dr. Alex Stivala; Co-PIs; University of Melbourne

Funding

SNF;

Status
Ongoing
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