Evidence map›Paper›PMID 40787607›Full record

ArticleJournal of the American Statistical Association2025

A Model-Agnostic Graph Neural Network for Integrating Local and Global Information.

Wenzhuo Zhou, Annie Qu, Keiland W Cooper, Norbert Fortin, Babak Shahbaba

Abstract read
In one paragraph

Article in Journal of the American Statistical Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Neurodatascience: Past, Present, and Future.Data science in science · 2026
    Article
  2. Article
  3. Article
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.

Wenzhuo ZhouDepartment of Statistics, University of California Irvine.
Annie QuDepartment of Statistics, University of California Irvine.
Keiland W CooperDepartment of Neurobiology and Behavior, University of California Irvine.
Norbert FortinDepartment of Neurobiology and Behavior, University of California Irvine.
Babak ShahbabaDepartment of Statistics, University of California Irvine.

Funding

Nicotinic enhancement of auditory-cognitive processingR01DC017687 · NIDCD · UNIVERSITY OF CALIFORNIA-IRVINE · PI METHERATE, RAJU · 2019 to 2023
$3.1M
Scalable Bayesian Stochastic Process Models for Neural Data AnalysisR01MH115697 · NIMH · UNIVERSITY OF CALIFORNIA-IRVINE · PI SHAHBABA, BABAK · 2018 to 2022
$1.7M
NIDCD NIH HHS R01 DC017687NIMH NIH HHS R01 MH115697
6 · The paper itself

Abstract

Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limitations: a lack of interpretability in their results due to their black-box nature, and an inability to learn representations of varying orders. To tackle these issues, we propose a novel

Indexed as

Empirical Rademacher complexityGraph representationInformation aggregation

Identifiers

PMID40787607
PMCPMC12333528

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.