Graph Domain Adaptation via Theory-Grounded Spectral Regularization.

Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen

Abstract read

In one paragraph

Article in ... International Conference on Learning Representations, 2023. 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

4 authors.

Yuning YouDepartment of Electrical and Computer Engineering, Texas A&M University.

Tianlong ChenDepartment of Electrical and Computer Engineering, University of Texas at Austin.

Zhangyang WangDepartment of Electrical and Computer Engineering, University of Texas at Austin.

Yang ShenDepartment of Electrical and Computer Engineering, Texas A&M University.

Funding

Unraveling molecular and system-level mechanisms of human disease-associated protein mutationsR35GM124952 · NIGMS · TEXAS ENGINEERING EXPERIMENT STATION · PI Yang Shen · 2017 to 2026

$2.9M

NIGMS NIH HHS R35 GM124952

6 · The paper itself

Abstract

Transfer learning on graphs drawn from varied distributions (domains) is in great demand across many applications. Emerging methods attempt to learn domain-invariant representations using graph neural networks (GNNs), yet the empirical performances vary and the theoretical foundation is limited. This paper aims at designing theory-grounded algorithms for graph domain adaptation (GDA). (

Identifiers

PMID42416498

PMCPMC13339966

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Registered trials

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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.