Leila Mirtajadini - PhD, Department of Economics, Social Sciences and Economics Faculty, Alzahra University, Tehran, Iran
Shamsollah Shirin Bakhsh - Associate professor, Department of Economics, Social Sciences and Economics Faculty, Alzahra University,Tehran, Iran
Mir Hossein Mousavi - Associate professor, Department of Economics, Social Sciences and Economics Faculty, Alzahra University, Tehran, Iran

This study tries to analyse the global electricity trade network by considering nations as nodes and trade among them as links. The data were derived from electricity export and import time series for the period ۲۰۱۰-۲۰۱۸. Network Theory is the basic approach to analyse the relationship between different countries. There are some metrics to scrutinize the global electricity trade network structure; namely node degree, betweenness centrality, cluster coefficient, dominating sets, and link prediction. Also, community detection helps us understand the position of each country in its sub-network. This analysis examines the main players in the electricity trade market. Dominating sets identify a group of nodes that play an active part in facilitating trade. As a result of link prediction analysis, it is very easy to identify missing links in the network. It is a very innovative method of estimating possible links among nodes and predicting the future of trade relations.

Electricity Trade, Network analysis, community, Centrality Measures, Link Prediction