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DTSTART:20211031T030000
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UID:calendar.24026.field_data.0@glad.uniroma1.it
DTSTAMP:20260406T055223Z
CREATED:20220201T174759Z
DESCRIPTION:AbstractGraph Neural Networks (GNNs) are a wide class of connec
 tionist models for graph processing. Recent studies have linked the expres
 sive power of GNNs to the Weisfeiler--Lehman algorithm\, which is a method
  of verifying whether two graphs are isomorphic or not. On the other hand\
 , it was also observed that the computational power of GNNs is related to 
 the unfolding trees\,  namely  trees that can be constructed by visiting t
 he graph from a given node. In this paper\, we unify these two theories an
 d prove that the  Weisfeiler--Lehman test and the unfolding trees induce t
 he same  equivalence relationship on the graph nodes: such an equivalencee
 xactly explains which nodes can or cannot be distinguished by a GNN. Moreo
 ver\, it is proved that GNNs can approximate in probability\, up  to  any 
  precision\,  any  function  on  graphs  that  respects  the  above mentio
 ned equivalence relationship. These results provide a more comprehensive u
 nderstanding of the computational power of GNNs in node classification/reg
 ression tasksYou can also join online at https://meet.google.com/mja-amts-
 tze?authuser=0&hs=122 
DTSTART;TZID=Europe/Paris:20220216T143000
DTEND;TZID=Europe/Paris:20220216T143000
LAST-MODIFIED:20240108T112834Z
LOCATION:Aula B203 DIAG and online
SUMMARY:The expressive power of Graph Neural Networks - A unifying point of
  view - Giuseppe Alessio D'Inverno
URL;TYPE=URI:http://glad.uniroma1.it/node/24026
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