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<title><string language="fre"><![CDATA[Spectral embedding for graph classification (workshop ERC Nemo Processus ponctuels et graphes aléatoires unimodulaires)]]></string></title>
<language>ENG</language>
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<string language="fre"><![CDATA[Learning on graphs requires a graph 
feature representation able to discriminate among different graphs while
being amenable to fast computation. The graph isomorphism problem tells
us that no fast representation of graphs is known if we require the 
representation to be both invariant to nodes permutation and able to 
discriminate two non-isomorphic graphs. Most graph representations 
explored so far require to be invariant. We explore new graph 
representations by relaxing this constraint. We present a generic 
embedding of graphs relying on spectral graph theory called Spectral 
Graph Embedding (SGE). We show that for a large family of graphs, our 
embedding is still invariant. To evaluate the quality and utility of our
SGE, we apply them to the graph classification problem.]]></string></description>
<keyword><string language="fre"><![CDATA[processus ponctuels]]></string></keyword><keyword><string language="fre"><![CDATA[graphes aléatoires]]></string></keyword><keyword><string language="fre"><![CDATA[dynamique des réseaux stochastiques]]></string></keyword><keyword><string language="fre"><![CDATA[modélisation réseau]]></string></keyword>
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<date><dateTime>2019-03-20</dateTime></date>
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<date><dateTime>2019-03-20</dateTime></date>
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<entity><![CDATA[BEGIN:VCARD
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<description>
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<string language="fre"><![CDATA[Workshop Processus ponctuels et graphes aléatoires unimodulaires (20-22 mars 2019)]]></string>
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<string language="fre"><![CDATA[Universités Numériques Thématiques 2009 http://www.universites-numeriques.fr]]></string>
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<string language="eng">DDC 22nd ed.</string>
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<string language="fre"><![CDATA[Probabilités, Statistiques mathématiques, Mathématiques appliquées]]></string>
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