DyGnROLE: Modeling Asymmetry in Dynamic Graphs with Node-Role-Oriented Latent Encoding | ScienceToStartup | ScienceToStartup

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References (18)

[1]

DVGMAE: Self-Supervised Dynamic Variational Graph Masked Autoencoder

2025Mengzhou Gao, Xinxun Zhang et al.

[2]

DyGMAE: A Novel Dynamic Graph Masked Autoencoder for Link Prediction

2025Weixiong Liu, Junwei Cheng et al.

[3]

DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs

Founder's Pitch

"DyGnROLE leverages role-aware modeling to enhance dynamic graph learning for asymmetrically behaving nodes."

Graph Learning•Score: 5•View PDF ↗

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DyGnROLE: Modeling Asymmetry in Dynamic Graphs with Node-Role-Oriented Latent Encoding

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References (18)

Founder's Pitch

"DyGnROLE leverages role-aware modeling to enhance dynamic graph learning for asymmetrically behaving nodes."

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DyGnROLE: Modeling Asymmetry in Dynamic Graphs with Node-Role-Oriented Latent Encoding

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References (18)

Founder's Pitch

"DyGnROLE leverages role-aware modeling to enhance dynamic graph learning for asymmetrically behaving nodes."

Commercial Viability Breakdown

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