  The modeling of networks, specifically generative models, have been shown to
provide a plethora of information about the underlying network structures, as
well as many other benefits behind their construction. Recently there has been
a considerable increase in interest for the better understanding and modeling
of networks, but the vast majority of this work has been for unsigned networks.
However, many networks can have positive and negative links(or signed
networks), especially in online social media, and they inherently have
properties not found in unsigned networks due to the added complexity.
Specifically, the positive to negative link ratio and the distribution of
signed triangles in the networks are properties that are unique to signed
networks and would need to be explicitly modeled. This is because their
underlying dynamics are not random, but controlled by social theories, such as
Structural Balance Theory, which loosely states that users in social networks
will prefer triadic relations that involve less tension. Therefore, we propose
a model based on Structural Balance Theory and the unsigned Transitive Chung-Lu
model for the modeling of signed networks. Our model introduces two parameters
that are able to help maintain the positive link ratio and proportion of
balanced triangles. Empirical experiments on three real-world signed networks
demonstrate the importance of designing models specific to signed networks
based on social theories to obtain better performance in maintaining signed
network properties while generating synthetic networks.
