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Optimus graph SSL matches GCN with 5x fewer labels

By PulseAugur Editorial · [1 sources] ·

A new training-free method for semi-supervised learning on graphs, named Optimus, has been developed. This approach matches the performance of Graph Convolutional Networks (GCNs) while requiring significantly fewer labeled data points, specifically five times fewer. A live demo is available on Hugging Face Spaces, allowing users to test the system with varying numbers of labels and even on their own datasets. AI

IMPACT This method could significantly reduce the cost and effort required for training graph-based machine learning models by minimizing the need for extensive labeled data.

RANK_REASON The cluster describes a new method for semi-supervised learning on graphs, presented with performance metrics and a live demo, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Optimus graph SSL matches GCN with 5x fewer labels

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The cluster describes a new method for semi-supervised learning on graphs, presented with performance metrics and a live demo, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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157 days old
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COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/Loner_Indian ·

    Training-free graph SSL matches GCN with 5× fewer labels — live demo [P]

    <!-- SC_OFF --><div class="md"><p>Hi all,</p> <p>I have been working on this method based on a hunch along with many llm for quite some time. Though first it was being engineered by me but I was learning in supervised ml area but this hunch took to semi-supervised ml and that to …

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