Evaluating motif projections.

Evaluating motif projections.

<p>Prediction differences (<i>y</i><sub>*</sub> − <i>y</i><sub><i>L</i>,<i>G</i></sub>) to the original label (<i>y</i><sub><i>L</i></sub>) is calculated for subgraph covering and non-co...

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Bibliographic Details
Main Author:	Bettina Soós (10860933) (author)
Other Authors:	Gonzalo Nápoles (22127303) (author), Pieter Spronck (13717717) (author), Çiçek Güven (8541117) (author)
Published:	2025
Subjects:	Biochemistry Neuroscience Evolutionary Biology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified statistical occurrence frequencies model &# 8217 core reasoning behind overly complex subgraphs subgraph coverings correspond objective optimization problem corresponding subgraph patterns identifying relevant subgraphs method outperforms state hence avoiding redundant graphs </ p deemed key properties also contribute meaningfully explainable machine learning possible subgraph space determining graphlet explanations graph prediction tasks machine learning subgraph explanations relevant due hard problem graphlet motifs covering subgraph explanatory subgraphs learning task explanatory subgraph xlink "> study introduces structured data still capturing single motifs resulting motifs rely solely prediction task possible motifs motifs found hoc approach genetic algorithm generating explanations desirable explanations computationally difficult comparing performance building blocks art techniques art methodologies also necessary
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Summary:	<p>Prediction differences (<i>y</i><sub>*</sub> − <i>y</i><sub><i>L</i>,<i>G</i></sub>) to the original label (<i>y</i><sub><i>L</i></sub>) is calculated for subgraph covering and non-covering after motif projection to evaluate fidelity. Larger absolute deviation indicate smaller fidelity to the label (or prediction, <i>y</i><sub><i>G</i></sub>).</p>

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