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Alana Shine

dblp:167/3989 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 100%
Theoretical computer science
1 paper
Graph algorithms and graph theory · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › structured data mining › graph mining
graph generation		 0.412019
Generative Graph Models based on Laplacian Spectra? · WWW 2019
Data mining › structured data mining
graph mining		 0.412019
Generative Graph Models based on Laplacian Spectra? · WWW 2019
Graph algorithms and graph theory
random graph generation		 0.412019
Generative Graph Models based on Laplacian Spectra? · WWW 2019

Methods — techniques the papers use, named apart from their topics

spectral graph theory · 0.8
YearPublicationVenuePosition
2019 Generative Graph Models based on Laplacian Spectra?
abstract
We present techniques for generating random graphs whose Laplacian spectrum approximately matches that of a given input graph. The motivation for matching the Laplacian spectrum is that it naturally encodes high-level connectivity information about the input graph; most existing models (e.g., variants of the Configuration Model, Stochastic Block Model, or Kronecker Graphs) focus on local structure or limited high-level partitions.
Alana Shine, David Kempe 0001
 WWW1