1、CONFIDENTIALITY NOTICE:This document is intended only for the use of the recipients to which Equinix sends it.Kartikeya SharmaSenior Associate Information Security Engineer at EquinixCONFIDENTIALITY NOTICE:This document is intended only for the use of the recipients to which Equinix sends it.What ar
2、e Graph Neural Networks?CONFIDENTIALITY NOTICE:This document is intended only for the use of the recipients to which Equinix sends it.What are Graph Neural Networks?Graph Neural Networks are powerful AI tools that learn from connected data,helping us uncover hidden patterns in complex networks.CONFI
3、DENTIALITY NOTICE:This document is intended only for the use of the recipients to which Equinix sends it.What are Graph Neural Networks?Nodes(also known as vertices)represent entities or objects in a graph.Edgesrepresent the relationships or connections between nodes.CONFIDENTIALITY NOTICE:This docu
4、ment is intended only for the use of the recipients to which Equinix sends it.What are Graph Neural Networks?GNNs learn rich node representations,calledembeddings using Message PassingCONFIDENTIALITY NOTICE:This document is intended only for the use of the recipients to which Equinix sends it.What a
5、re Graph Neural Networks?GNNs have found applications in various domains,including:Social network analysis Molecular property prediction Knowledge graph completion Recommender systemsCONFIDENTIALITY NOTICE:This document is intended only for the use of the recipients to which Equinix sends it.GNNs vs
6、 Traditional Neural NetworksAspectGraph Neural NetworksTraditional Neural NetworksInput StructureGraphs with variable size and connectivityFixed-size,grid-like input(e.g.,images,sequences)RelationshipsModels and learns from relationships between entitiesAssumes independence between input featuresNod