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Conference item

Sampling and inference for beta neutral-to-the-left models of sparse networks

Abstract:

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...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
ORCID:
0000-0001-8581-4350
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
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Funding agency for:
Bloem-Reddy, B
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Funding agency for:
Mathieu, E
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Publisher:
AUAI Press Publisher's website
Pages:
477-486
Publication date:
2018-08-04
Acceptance date:
2018-05-15
Pubs id:
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

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