  Substantive research in the Social Sciences regularly investigates signed
networks, where edges between actors are either positive or negative. For
instance, schoolchildren can be friends or rivals, just as countries can
cooperate or fight each other. This research often builds on structural balance
theory, one of the earliest and most prominent network theories, making signed
networks one of the most frequently studied matters in social network analysis.
While the theorization and description of signed networks have thus made
significant progress, the inferential study of tie formation within them
remains limited in the absence of appropriate statistical models. In this paper
we fill this gap by proposing the Signed Exponential Random Graph Model
(SERGM), extending the well-known Exponential Random Graph Model (ERGM) to
networks where ties are not binary but negative or positive if a tie exists.
Since most networks are dynamically evolving systems, we specify the model for
both cross-sectional and dynamic networks. Based on structural hypotheses
derived from structural balance theory, we formulate interpretable signed
network statistics, capturing dynamics such as "the enemy of my enemy is my
friend". In our empirical application, we use the SERGM to analyze cooperation
and conflict between countries within the international state system.
