Mitigating topology biases in Graph Diffusion via Counterfactual Intervention | ScienceToStartup | ScienceToStartup

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References (33)

[1]

Graphusion: Latent Diffusion for Graph Generation

2024Ling Yang, Zhilin Huang et al.

[2]

Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models

2024Iakovos Evdaimon, Giannis Nikolentzos et al.

[3]

Autoregressive Diffusion Model for Graph Generation

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"FairGDiff provides a counterfactual-based, debiasing solution for graph diffusion models to ensure fairness in graph generation tasks."

Graph AI•Score: 6•View PDF ↗

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Mitigating topology biases in Graph Diffusion via Counterfactual Intervention

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References (33)

Founder's Pitch

"FairGDiff provides a counterfactual-based, debiasing solution for graph diffusion models to ensure fairness in graph generation tasks."

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Mitigating topology biases in Graph Diffusion via Counterfactual Intervention

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References (33)

Founder's Pitch

"FairGDiff provides a counterfactual-based, debiasing solution for graph diffusion models to ensure fairness in graph generation tasks."

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