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Exponential family random graph models
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<p>Exponential family random graph models (ERGMs) are a set of <a href="/facts/Statistical_model/jKkT5ftm">statistical models</a> used to study the structure and patterns within <a href="/facts/Network_theory/m6CQovwm">networks</a>, such as those in social, organizational, or scientific contexts.They analyze how connections (<a href="/facts/Edge_(graph_theory)/W0MqKkyE">edges</a>) form between individuals or entities (<a href="/facts/Node_(graph_theory)/WhlWmL3o">nodes</a>) by modeling the likelihood of network features, like <a href="/facts/Clustering_(statistics)/HUeDnWPt">clustering</a> or <a href="/facts/Centrality/4n24KqLt">centrality</a>, across diverse examples including <a href="/facts/Knowledge_sharing/c6arhqU4">knowledge networks</a>, organizational networks, colleague networks, <a href="/facts/Social_media/kUIkfpIA">social media</a> networks, networks of scientific collaboration, and more. Part of the <a href="/facts/Exponential_family/1LkkqEIf">exponential family</a> of distributions, ERGMs help researchers understand and predict network behavior in fields ranging from sociology to <a href="/facts/Data_science/AHYUo0Rn">data science</a>.</p>