4,000 research outputs found
Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models
Exponential-family random graph models (ERGMs) provide a principled way to
model and simulate features common in human social networks, such as
propensities for homophily and friend-of-a-friend triad closure. We show that,
without adjustment, ERGMs preserve density as network size increases. Density
invariance is often not appropriate for social networks. We suggest a simple
modification based on an offset which instead preserves the mean degree and
accommodates changes in network composition asymptotically. We demonstrate that
this approach allows ERGMs to be applied to the important situation of
egocentrically sampled data. We analyze data from the National Health and
Social Life Survey (NHSLS).Comment: 37 pages, 2 figures, 5 tables; notation revised and clarified, some
sections (particularly 4.3 and 5) made more rigorous, some derivations moved
into the appendix, typos fixed, some wording change
- …