Across 10 random Citeseer training splits with fixed validation and test sets, SGC reports 71.40% test accuracy; GAT reports 72.6%, while SGC's reported variation is the largest among the listed non-daggered models.

Across 10 random Citeseer training splits with fixed validation and test sets, SGC reports 71.40% test accuracy; GAT reports 72.6%, while SGC's reported variation is the largest among the listed non-daggered models. · CiteArk

Measurement results

0/6 assessed

0 supported0 disputed or conflicting6 not assessed

The complete result group stays with its claim. Assessed rows come first; their judgments do not replace the whole-claim conclusion. Expand a row for conditions, sources, and evidence history.

Each row is a measurement with its reported value, latest available observation, and cumulative evidence state.
Metric and conditions	Reported	Latest observation	Evidence judgment
reportedPlusMinus: 2.9	70.67%	—	Not assessed
reportedPlusMinus: 2.27	66.56%	—	Not assessed
reportedPlusMinus: 0.81	67.26%	—	Not assessed
reportedPlusMinus: 0.95	71.04%	—	Not assessed
reportedPlusMinus: 0.58	72.6%	—	Not assessed
reportedPlusMinus: 3.92	71.4%	—	Not assessed

Across 10 random Citeseer training splits with fixed validation and test sets, SGC reports 71.40% test accuracy; GAT reports 72.6%, while SGC's reported variation is the largest among the listed non-daggered models.

Reported and observed measurements

Assessments (0)

Runs (0)

Measurement results