What is recorded here?
Removed intermediate nonlinearities and collapsed feature transformations in GCN-style models, precomputing repeated graph propagation before fitting a linear classifier.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits GCN and graph low-pass filtering. Matching selected benchmarks does not establish that nonlinearities are unnecessary for all graph tasks or preserve full GCN expressivity.
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People and systems
Felix Wu; Tianyi Zhang; Amauri Holanda de Souza Jr.; Christopher Fifty; Tao Yu; Kilian Q. Weinberger
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
SGC: fixed graph propagation followed by linear classification
Removed intermediate nonlinearities and collapsed feature transformations in GCN-style models, precomputing repeated graph propagation before fitting a linear classifier.
Scientific scope
Inherits GCN and graph low-pass filtering. Matching selected benchmarks does not establish that nonlinearities are unnecessary for all graph tasks or preserve full GCN expressivity.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Felix Wu; Tianyi Zhang; Amauri Holanda de Souza Jr.; Christopher Fifty; Tao Yu; Kilian Q. Weinberger
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.