Grables: When Graph Structure Helps Tabular Prediction

Tamara Cucumides; Floris Geerts

cs.LGFeb 3, 2026

Grables: When Graph Structure Helps Tabular Prediction

Authors: Tamara Cucumides, Floris Geerts

Organizations: Department of Computer Science, University of Antwerp. Belgium.

Abstract

Simple row-wise predictors that score each row independently remain competitive with far more expressive architectures on tabular benchmarks, which are i.i.d. by construction. They cannot, however, represent natural targets on transactional, temporal, and relational tables, where labels depend on other rows. We show that row-wise prediction provably rules out targets driven by global counts, overlaps, and relational patterns. To make ``using structure'' precise across architectures, we introduce grables: a modular interface that separates how a table is lifted to a graph (constructor) from how predictions are computed on that graph (node predictor), placing gradient-boosted trees, neighbourhood aggregation, transformer-style tabular models, and relational GNNs on a single axis and pinpointing where expressive power comes from. The framework yields a hierarchy that answers when structure is needed: row-local models suffice for extension-invariant targets, lightweight neighbourhood aggregation captures shallow inter-row signals, and explicit graph construction is needed for compositional patterns that fixed aggregations cannot express without exponential feature blow-up. Experiments on synthetic, transactional, and relational data confirm the predicted separations, but also show that benchmark performance is a poor proxy for structural capability: row-local models score well on tasks they provably cannot represent, and a stress-test that suppresses row-local cues is needed to expose the gap. Conversely, access to cross-row context does not imply a model uses it. Feeding explicitly extracted inter-row structure to strong tabular learners yields gains exactly where the hierarchy predicts. The practical recipe is to add structure incrementally, and only where the target is extension-sensitive.

Figures & tables

Figure 1: Incidence-grable patterns for the four logical tasks. Row nodes (circles) connect to column–value nodes (squares) via typed edges; the target row

r

, whose label is predicted, is filled. We write

d

for the degree of a column–value node, i.e. the number of rows carrying that value. (a) Unique

(r)

r

is adjacent to a value node of degree

1

; the second value node, of degree

2

, is not a witness. (b) Count

{}_{N}(r)

r

is adjacent to a value node of degree greater than

N

, i.e. at least

N

other rows share that value. (c) Double

(r)

: a length-3 pattern

r-u_{i,v}-s-u_{j,w}

whose witness row

s

satisfies the local condition

P

. (d) Diamond

(r)

: a single row

s

shares two distinct column–value nodes with

r

. In all four cases the label of

r

depends on rows other than

r

Synthetic transactions
Model	Unique		Count		Count ( $=$ )		Double		Diamond
	AUC	F1	AUC	F1	AUC	F1	AUC	F1	AUC	F1
LightGBM	0.566	0.123	0.889	0.000	0.675	0.314	0.789	0.729	0.846	0.682
RealMLP	0.516	0.123	0.933	0.733	0.521	0.000	0.785	0.686	0.796	0.603
ModernNCA	0.597	0.000	0.862	0.569	0.544	0.000	0.720	0.670	0.798	0.593
TabPFN2.5	0.823	0.025	0.962	0.775	0.340	0.055	0.670	0.699	0.868	0.702

Table 1: Diagnostic comparison on logical tasks in synthetic and transaction datasets. F1 of

0

corresponds to predicting

0

for all instances. Results averaged over 5 runs, variance in Appendix G.1

Figure 2: F1 score on the stress-test set of the synthetic transactions data. The stress-test set removes the row-local cues that correlate with the labels in the original test split (Appendix G.2.2 ).

Appendix figures & tables20 assets

Supplementary material from the paper’s appendix.

Appendix

id	card_id	merchant_id	merchant_city
1	C000317	42	Brussels
2	C000102	17	ONLINE
3	C000317	105	Paris
4	C000891	42	Brussels
5	C000045	311	Berlin
6	C000102	17	ONLINE

Appendix

Table 3: Example rows from the synthetic transaction dataset.

Statistic	Train	Validation	Test
# transactions	8,000	1,000	1,000
# unique cards	2,500	350	350
# unique merchants	3,500	300	300
Online merchants	0.15	0.12	0.12

Appendix

Table 4: Summary statistics of the synthetic transaction datasets.

InvoiceNo	StockCode	Description	Qty	InvoiceDate	Price	CustomerID	Country
581220	16225	Rattle Snake Eggs	24	2011-12-08	0.39	13924	UK
581220	16258A	Circular Rubbers	36	2011-12-08	0.19	13924	UK
581220	21411	Heart Doorstop (Red)	8	2011-12-08	1.95	13924	UK

Appendix

Table 5: Illustrative example of transaction rows (synthetic but representative). Multiple rows correspond to distinct items within the same invoice.

Attribute	Value
Domain	Medical
Number of tables	15
Number of rows	5,852,157
Number of columns	140
Start date	2000-01-01
Validation timestamp	2020-01-01

Appendix

Table 6: Summary statistics of the relbench-trial dataset.

Figure 4: Relational schema of the relbench-trial relational dataset. The task studied is associated with table studies that contain descriptive information about the clinical studies. Taken from https://relbench.stanford.edu/datasets/rel-trial/

Dataset	Task	Train (%)	Val (%)	Test (%)
Synthetic Data	UNIQUE	13.1	6.6	7.5
Synthetic Data	COUNT	24.9	18.0	18.4
Synthetic Data	COUNT (=)	15.4	15.0	14.1
Synthetic Data	DOUBLE	48.2	42.8	43.4
Synthetic Data	DIAMOND	30.7	28.1	24.6
Retail Data	UNIQUE	0.9	14.2	10.5

Appendix

Table 7: Proportion of presence of label tasks across train, validation, and test splits for each dataset

Model	Aggregate AUC	Cases A and C AUC	Case B AUC
LightGBM	0.667	0.997	0.497
TabPFN	0.691	0.869	0.501
ModernNCA	0.662	0.961	0.495

Appendix

Table 8: Performance on COUNT >5 under single-point-with-reference protocol, decomposed by case. Aggregate metrics conflate memorization of value-label associations with extension-sensitive reasoning. Results averaged over 5 runs.

Task	Metric	Original	Finetuned	Abs. diff.	Rel. gain/loss
UNIQUE	roc_auc	0.8230	0.8744	+0.0514	+6.25%
UNIQUE	f1	0.0250	0.0240	-0.0010	-4.00%
COUNT	roc_auc	0.9620	0.9290	-0.0330	-3.43%
COUNT	f1	0.7750	0.8810	+0.1060	+13.68%
COUNT(=)	roc_auc	0.3400	0.3310	-0.0090	-2.65%
COUNT(=)	f1	0.0550	0.0137	-0.0413	-75.09%

Appendix

Table 9: Effect of finetuning for TabPFN on logical tasks in the synthetic data.

Hyperparameter	Uniqueness	Count ( $>$ )	Count ( $=$ )	Double condition	Diamond
Layers	2	2	3	3	4
Hidden dimension	64	64	128	64	64
Dropout	0.0	0.0	0.0	0.0	0.0
Learning rate	1e-3	1e-3	1e-3	1e-3	1e-3
Weight decay	1e-4	1e-4	1e-6	1e-4	1e-4
Aggregation	sum	sum	sum	sum	sum

Appendix

Table 10: Training hyperparameters for synthetic dataset across different tasks.

Model	Unique		Count		Count ( $=$ )
	AUC	F1	AUC	F1	AUC	F1
Synthetic transactions
LightGBM	0.566 $\pm$ .012	0.123 $\pm$ .007	0.889 $\pm$ .001	0.000 $\pm$ .000	0.675 $\pm$ .030	0.314 $\pm$ .009
RealMLP	0.516 $\pm$ .012	0.123 $\pm$ .007	0.933 $\pm$ .005	0.733 $\pm$ .002	0.521 $\pm$ .007	0.000 $\pm$ .000
ModernNCA	0.597 $\pm$ .009	0.000 $\pm$ .000	0.862 $\pm$ .017	0.569 $\pm$ .007	0.544 $\pm$ .012	0.000 $\pm$ .000
TabPFN2.5	0.823 $\pm$ .007	0.025 $\pm$ .003	0.962 $\pm$ .008	0.775 $\pm$ .004	0.340 $\pm$ .020	0.055 $\pm$ .004

Appendix

Table 11: Diagnostic comparison on logical tasks in synthetic and transaction datasets (Part 1: Unique , Count , Count (

=

)). F1 of

0

corresponds to predicting

0

for all instances. Results averaged over 5 runs (mean ± std).

Synthetic transactions
Model	Double		Diamond
	AUC	F1	AUC	F1
LightGBM	0.789 $\pm$ .007	0.729 $\pm$ .012	0.846 $\pm$ .021	0.682 $\pm$ .009
RealMLP	0.785 $\pm$ .012	0.686 $\pm$ .030	0.796 $\pm$ .021	0.603 $\pm$ .017
ModernNCA	0.720 $\pm$ .033	0.670 $\pm$ .017	0.798 $\pm$ .009	0.593 $\pm$ .005
TabPFN2.5	0.670 $\pm$ .025	0.699 $\pm$ .013	0.868 $\pm$ .030	0.702 $\pm$ .018

Appendix

Table 12: Diagnostic comparison on logical tasks in synthetic and transaction datasets (Part 2: Double , Diamond ). F1 of

0

corresponds to predicting

0

for all instances. Results averaged over 5 runs (mean ± std).

Figure 5: Box-plots for F1 score of models LightGBM, RealMLP, Sage and TabPFN in 5 different perturbations of the original test set.

F1-score
Model	$t_{1}$ : unique			$t_{2}$ : count( $>$ )			$t_{3}$ : count( $=$ )
	Orig	Clean	$\boldsymbol{\Delta}$	Orig	Clean	$\boldsymbol{\Delta}$	Orig	Clean	$\boldsymbol{\Delta}$
GNN (SAGE)	0.988	0.989	+0.001	0.967	0.997	+0.030	0.171	1.000	+0.829
LightGBM	0.190	0.190	+0.000	0.000	0.000	+0.000	0.027	0.027	+0.000
RealMLP	0.190	0.191	+0.001	0.881	0.885	+0.004	0.027	0.027	+0.000
TabPFN	0.404	0.406	+0.002	0.001	0.001	-0.000	0.023	0.019	-0.004

Appendix

Table 13: Performance on original vs. cleaned retail data (Unique, Count).

\Delta=\text{Clean}-\text{Orig}

F1-score
Model	$t_{3}$ : double condition			$t_{4}$ : diamond
	Orig	Clean	$\boldsymbol{\Delta}$	Orig	Clean	$\boldsymbol{\Delta}$
GNN (SAGE)	0.863	0.855	-0.008	0.251	0.249	-0.002
LightGBM	0.815	0.672	-0.143	0.199	0.087	-0.112
RealMLP	0.000	0.819	+0.819	0.139	0.169	+0.030
TabPFN	0.003	0.003	+0.000	0.007	0.007	+0.000

Appendix

Table 14: Performance on original vs. cleaned retail data (Double, Diamond).

\Delta=\text{Clean}-\text{Orig}

Figure 6: Permutation feature importance based on ROC-AUC score for the AutoGluon tabular model. Bars show the mean change in ROC-AUC score under feature permutation, with 99% confidence intervals from repeated shuffles. Positive values indicate performance degradation, while negative values indicate improved performance after permutation.

Figure 7: Permutation feature importance based on ROC-AUC score for the AutoGluon tabular model with tabular time-aware neighbor feature aggregation features. Bars show the mean change in ROC-AUC score ( $\Delta$ ROC-AUC) under feature permutation, with 99% confidence intervals from repeated shuffles. Trimmed to most relevant 15 attributes.

Figure 8: Permutation feature importance based on ROC-AUC score for the AutoGluon tabular model augmented with embeddings from a GNN trained on the incidence grable from the table. Bars show the mean change in ROC-AUC score ( $\Delta$ ROC-AUC) under feature permutation, with 99% confidence intervals from repeated shuffles. Trimmed to most relevant 15 attributes. Important to note that out of the 15 most important attributes, 9 correspond to GNN embeddings.

Figure 9: Permutation feature importance based on ROC-AUC score for the AutoGluon tabular model augmented with embeddings from a GNN trained on the relational entity graph from the entire database. Bars show the mean change in ROC-AUC score ( $\Delta$ ROC-AUC) under feature permutation, with 99% confidence intervals from repeated shuffles. Trimmed to most relevant 15 attributes. Important to note that out of the 15 most important attributes, 7 correspond to GNN embeddings.

Dataset ID	# Cat. features	Tabular	Hybrid	$\Delta$
363619	4	0.845 $\pm$ .02	0.848 $\pm$ .02	$+0.36\%$
363623	4	0.928 $\pm$ .02	0.924 $\pm$ .01	$-0.43\%$
363626	13	0.717 $\pm$ .01	0.752 $\pm$ .01	$+4.88\%$
363671	3	0.784 $\pm$ .01	0.800 $\pm$ .01	$+2.04\%$
363682	8	0.680 $\pm$ .02	0.698 $\pm$ .02	$+2.65\%$
363684	8	0.890 $\pm$ .01	0.885 $\pm$ .01	$-0.56\%$

Appendix

Table 15: ROC-AUC on 9 TabArena datasets selected for having a binary target and at least 3 categorical features with non-unique values. Results averaged over 5 runs (mean

\pm

std).

Dataset	Task	Tabular	GNN	Hybrid	% vs. Tab	% vs. GNN
f1	driver-top3	0.739 $\pm$ .01	0.795 $\pm$ .01	0.824 $\pm$ .01	$+11\%$	$+3\%$
f1	driver-dnf	0.686 $\pm$ .02	0.649 $\pm$ .01	0.691 $\pm$ .01	$+1\%$	$+6\%$
arxiv	paper-citation	0.712 $\pm$ .04	0.807 $\pm$ .01	0.818 $\pm$ .01	$+14\%$	$+1\%$
stack	user-engagement	0.634 $\pm$ .03	0.835 $\pm$ .02	0.894 $\pm$ .02	$+41\%$	$+7\%$
stack	user-badge	0.634 $\pm$ .02	0.668 $\pm$ .02	0.811 $\pm$ .01	$+27\%$	$+21\%$

Appendix

Table 16: ROC-AUC on 5 additional RelBench binary classification tasks, re-implemented in a unified setup for fair comparison. Absolute numbers differ from published results due to text feature treatment; relative comparisons are unaffected. The hybrid matches or improves over both purely tabular and purely graph baselines. Results averaged over 5 runs (mean

\pm

std).

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