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Evidence record 14486 · automatically gathered

Unsupervised Graph Representation Learning with Complementary View Alignment

Unsupervised graph representation learning aims to derive meaningful node embeddings by capturing both structural and attribute information without relying on labeled data. Existing methods, such as GAEs, have demonstrated effectiveness but typically rely on message-passing mechanisms that assume homophily, leading to performance degradation on heterophilous graphs, where connected nodes exhibit dissimilar features. This homophily bias results in the loss of critical high-frequency components th

Record details

Published: 27 July 2026
Source: arXiv cs.LG
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 29 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Safety & alignment

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Shared topic: Bias & fairness · Shared topic: Safety & alignment

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Shared topic: Bias & fairness · Shared topic: Safety & alignment

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arXiv fairness query · 25 July 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

Sympathetic Framing: Evaluating AI Alignment across Sociodemographic Groups

arXiv cs.CY · 31 July 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

Spectral Prior for Reducing Exposure Bias in Diffusion Models

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Shared topic: Bias & fairness · Shared topic: Safety & alignment

Inference-Time Policy Alignment for Fair Reinforcement Learning

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How to cite this record

ethics.ai (27 July 2026), “Unsupervised Graph Representation Learning with Complementary View Alignment,” evidence record 14486, https://ethics.ai/record/14486 (originally published by arXiv cs.LG).

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