Name
Abstract | ||
|---|---|---|
Empirical evidence suggests that heavy-tailed degree distributions occurring in many real networks are well-approximated by power laws with exponents eta that may take values either less than and greater than two. Models based on various forms of exchangeability are able to capture power laws with eta < 2, and admit tractable inference algorithms; we draw on previous results to show that eta > 2 cannot be generated by the forms of exchangeability used in existing random graph models. Preferential attachment models generate power law exponents greater than two, but have been of limited use as statistical models due to the inherent difficulty of performing inference in non-exchangeable models. Motivated by this gap, we design and implement inference algorithms for a recently proposed class of models that generates eta of all possible values. We show that although they are not exchangeable, these models have probabilistic structure amenable to inference. Our methods make a large class of previously intractable models useful for statistical inference. |
Year | Venue | DocType |
|---|---|---|
2018 | UNCERTAINTY IN ARTIFICIAL INTELLIGENCE | Conference |
Volume | Citations | PageRank |
abs/1807.03113 | 0 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin Bloem-Reddy | 1 | 0 | 0.34 |
| Adam Foster | 2 | 0 | 1.69 |
| Emile Mathieu | 3 | 3 | 1.72 |
| Yee Whye Teh | 4 | 6253 | 539.26 |