- Abstract:
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Empirical evidence suggests that heavy-tailed degree distributions occurring in many real networks are well-approximated by power laws with exponents η 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 η < 2, and admit tractable inference algorithms; we draw on previous results to show that η > 2 cannot be generated by the forms of exchangeability used in existing random graph models. Preferen...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted Manuscript
- Files:
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(pdf, 1.3mb)
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- Funding agency for:
- Foster, A
- Funding agency for:
- Mathieu, E
- Publisher:
- AUAI Press Publisher's website
- Pages:
- 477-486
- Publication date:
- 2018-08-04
- Acceptance date:
- 2018-05-15
- Pubs id:
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pubs:879305
- URN:
-
uri:49598f78-0691-4281-86c5-895a26a62b92
- UUID:
-
uuid:49598f78-0691-4281-86c5-895a26a62b92
- Local pid:
- pubs:879305
- ISBN:
- 978-0-9966431-3-9
- Copyright holder:
- AUAI Press
- Copyright date:
- 2018
- Notes:
-
Copyright © 2018 by AUAI Press. This is the accepted manuscript version of the paper. The final version is available online from AUAI Press at: http://auai.org/uai2018/
Conference item
Sampling and inference for beta neutral-to-the-left models of sparse networks
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