ERGM: What Creates Network Structure?
In modern social science, more and more phenomena are observed as networks. The spread of infectious diseases, the diffusion of knowledge and information, political and military alliances between states, and opinion formation on social media are all better explained not by individual attributes, but by how actors are connected. In this context, social network analysis has become a core tool for understanding complex social systems.
For a long time, however, social network analysis remained largely descriptive. Measures such as centrality or network visualization could describe the structure as an outcome, but they could not answer a deeper causal question: why did this network take this form in the first place?
Why do some networks contain many triangles while others do not? Is a given structure the result of chance, or of recurring social mechanisms?
Recent work in network science has increasingly focused on these questions. The goal is no longer just to observe relationships, but to statistically explain the tendencies and processes through which relationships form. Exponential Random Graph Models(ERGM), sit at the center of this shift.
ERGM treats an observed network not as a fixed outcome, but as the result of a probabilistic formation process. This article explains why ERGM is needed, what kinds of questions it makes possible, and why it has become an essential complement to traditional network analysis.