Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

Regularized preconditioned GMRES and the regularized iteration method

Applied Numerical Mathematics(2020)10.1016/j.apnum.2020.01.001Source: DataRank Database

Regularized preconditioned GMRES and the regularized iteration method is a research paper published in Applied Numerical Mathematics (2020). On theSindex it has a DataRank of 0.812. It has been cited 16 times, with 10 citing works in its 1-hop citation network.

N/A

0.812DataRank · unranked

0.812

16 citations · base score 2.8

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

›Data sources & pipeline

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Pending

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

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DataRank Breakdown

Base Score 52%Citation Network 48%

Base Score Contribution

0.425

From this paper's citation signal

Citation Network Contribution

0.387

From 7 citing papers with measurable signal

Learn more about DataRank methodology →

Top 2 citers driving the network score

Ranked by citation count — the same ordering the engine uses when summing log1p(C_q) over citers.

GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems
SIAM Journal on Scientific and Statistical Computing198611,009 citationsDataRank 1.4
A generalized variant of the deteriorated PSS preconditioner for nonsymmetric saddle point problems
Numerical Algorithms201715 citationsDataRank 0.879

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 52% comes from its base citations and 48% from the citation network (7 citing papers contributed measurable signal).

Base score B(p): log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
Network N(p): Σ over citers of log1p(C_q) ÷ max(outdegree_q, 1). Being cited by a highly-cited paper with few references counts most.
Damping factor d = 0.85: DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
Self-citations excluded: Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.

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Node colors:CenterData PaperData + Open AccessNon-dataSelected & links| Node size = percentile rank

Authors (1)

A. Badahmane