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How hard is to distinguish graphs with graph neural networks?
Andreas Loukas
Research output
:
Contribution to journal
›
Conference article
›
peer-review
31
Scopus citations
Overview
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Dive into the research topics of 'How hard is to distinguish graphs with graph neural networks?'. Together they form a unique fingerprint.
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Keyphrases
Graph Neural Network
100%
Order of Magnitude
33%
Tight
33%
Connected Graph
33%
Communication Capacity
33%
Actual Performance
33%
Node number
33%
Hardness Results
33%
Message Size
33%
Classification Task
33%
Global State
33%
Graph Isomorphism
33%
Message Passing Model
33%
Isomorphism Class
33%
Average-case
33%
Forward Pass
33%
Graph Classification
33%
Capacity Measures
33%
Computer Science
Graph Neural Network
100%
And-States
33%
Communication Capacity
33%
Connected Graph
33%
Message Passing
33%
Classification Task
33%
Actual Performance
33%