Evidence map›Paper›PMID 41180157›Full record

ArticleIEEE transactions on signal and information processing over networks2025

Learning Networks from Wide-Sense Stationary Stochastic Processes.

Anirudh Rayas, Jiajun Cheng, Rajasekhar Anguluri, Deepjyoti Deka, Gautam Dasarathy

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in IEEE transactions on signal and information processing over networks, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Anirudh RayasSchool of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ, USA.
Jiajun ChengSchool of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ, USA.
Rajasekhar AnguluriDepartment of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, MD 21250, USA.
Deepjyoti DekaEnergy Initiative at Massachusetts Institute of Technology, USA.
Gautam DasarathySchool of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ, USA.

Funding

Graphical Models from Partially Observed Interactions with Biomedical ApplicationsR01GM140468 · NIGMS · RICE UNIVERSITY · PI ALLEN, GENEVERA IRENE · 2020 to 2022
$600k
NIGMS NIH HHS R01 GM140468
6 · The paper itself

Abstract

Complex networked systems driven by latent inputs are common in fields like neuroscience, finance, and engineering. A key inference problem here is to learn edge connectivity from node outputs (potentials). We focus on systems governed by steady-state linear conservation laws:

Indexed as

Conservation lawsℓ1-regularized Whittle’s likelihood estimatorNetwork topology inferenceSpectral precision matrix

Identifiers

PMID41180157
PMCPMC12574584

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.