SIES learns generalizable local coupling operators via signed source-target attention for controllable synchronization in graph dynamical systems and applies the principle to heterophilous graph representation learning.
Classic gnns are strong baselines: Re- assessing gnns for node classification
2 Pith papers cite this work. Polarity classification is still indexing.
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Introduces quantitative error feedback from digital filter techniques to exactly compensate quantization noise in graph filtering, with closed-form optimal coefficients for deterministic, random-graph, and asynchronous scenarios.
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Swarm-Inspired Generation of Collective Behaviors in Graph Dynamical Systems
SIES learns generalizable local coupling operators via signed source-target attention for controllable synchronization in graph dynamical systems and applies the principle to heterophilous graph representation learning.
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Quantitative Error Feedback for Quantization Noise Reduction of Filtering over Graphs
Introduces quantitative error feedback from digital filter techniques to exactly compensate quantization noise in graph filtering, with closed-form optimal coefficients for deterministic, random-graph, and asynchronous scenarios.