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Gang Wu 0014

dblp:99/6515-14 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2013
0000-0002-4936-437XORCID · corroborated

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

Databases, data management, data science and information retrieval · 3 · 2 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
Information retrieval · 100%
Theoretical computer science
1 paper
Algorithms and data structures · 50% Mathematical optimization · 50%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › ranking › graph-based ranking
pagerank
0.112010
Arnoldi versus GMRES for computing pageRank: A theoretical contribution to google's pageRank problem · ACM Trans. Inf. Syst. 2010
Information retrieval
search engines
0.112010
Arnoldi versus GMRES for computing pageRank: A theoretical contribution to google's pageRank problem · ACM Trans. Inf. Syst. 2010
Mathematical optimization › iterative methods
krylov subspace methods
0.112010
Arnoldi versus GMRES for computing pageRank: A theoretical contribution to google's pageRank problem · ACM Trans. Inf. Syst. 2010
Algorithms and data structures
numerical linear algebra
0.112010
Arnoldi versus GMRES for computing pageRank: A theoretical contribution to google's pageRank problem · ACM Trans. Inf. Syst. 2010

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

arnoldi algorithm · 0.2GMRES · 0.2
YearPublicationVenuePosition
2013 A preconditioned conjugate gradient algorithm for GeneRank with application to microarray data mining
Gang Wu 0014, Yimin Wei 0001
Data Min. Knowl. Discov.1
2012 Lumping algorithms for computing Google's PageRank and its derivative, with attention to unreferenced nodes
Zhengke Miao, Gang Wu 0014, Yimin Wei 0001
Inf. Retr.3
2010 Arnoldi versus GMRES for computing pageRank: A theoretical contribution to google's pageRank problem
abstract
PageRank is one of the most important ranking techniques used in today's search engines. A recent very interesting research track focuses on exploiting efficient numerical methods to speed up the computation of PageRank, among which the Arnoldi-type algorithm and the GMRES algorithm are competitive candidates. In essence, the former deals with the PageRank problem from an eigenproblem, while the latter from a linear system, point of view. However, there is little known about the relations between the two approaches for PageRank. In this article, we focus on a theoretical and numerical comparison of the two approaches. Numerical experiments illustrate the effectiveness of our theoretical results.
Gang Wu 0014, Yimin Wei 0001
ACM Trans. Inf. Syst.1