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  3. RaDAR: Relation-aware Diffusion-Asymmetric Graph Contrastive
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RaDAR: Relation-aware Diffusion-Asymmetric Graph Contrastive Learning for Recommendation

Stale17d ago
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0.0/10

Compared to this week’s papers

Stale evidence

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: partial

Freshness: stale

Source paper: RaDAR: Relation-aware Diffusion-Asymmetric Graph Contrastive Learning for Recommendation

PDF: https://arxiv.org/pdf/2603.16800v1

Repository: https://github.com/ohowandanliao/RaDAR

Source count: 0

Coverage: 50%

Last proof check: 2026-03-19T20:22:24.288Z

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Paper Mode

RaDAR: Relation-aware Diffusion-Asymmetric Graph Contrastive Learning for Recommendation

Overall score: 7/10
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Canonical Paper Receipt

Last verification: 2026-03-19T20:22:24.288Z

Freshness: stale

Proof: partial

Repo: active

References: 0

Sources: 0

Coverage: 50%

Missingness
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Unknowns
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Starting…

Dimensions overall score 7.0

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Last commit
2/11/2026
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Keep exploring

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AC2L-GAD: Active Counterfactual Contrastive Learning for Graph Anomaly Detection
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Guiding Diffusion-based Reconstruction with Contrastive Signals for Balanced Visual Representation
Score 6.0down
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CoDCL: Counterfactual Data Augmentation Contrastive Learning for Continuous-Time Dynamic Network Link Prediction
Score 6.0down
Prior Work
Smoothing the Landscape: Causal Structure Learning via Diffusion Denoising Objectives
Score 7.0stable
Prior Work
Mitigating Homophily Disparity in Graph Anomaly Detection: A Scalable and Adaptive Approach
Score 7.0stable
Prior Work
FairGC: Fairness-aware Graph Condensation
Score 7.0stable
Prior Work
Relevance Feedback in Text-to-Image Diffusion: A Training-Free And Model-Agnostic Interactive Framework
Score 7.0stable

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