Text-Relational Graph Neural Networks (Text-RGNNs) for Text Classification

This repository contains the implementation of Text-Relational Graph Neural Networks (Text-RGNNs) presented in the paper Text-RGNNs: Relational Modeling for Heterogeneous Text Graphs published in IEEE Signal Processing Letters. The model leverages heterogeneous graph neural networks to capture complex relationships in text data, significantly improving performance on benchmark datasets.

Evaluation Results for Best Models with All Splits

Train Percentages

Dataset	Split	1%	5%	10%	20%	100%
cola	train	32.33	44.79	53.71	63.62	70.15
	val	26.49	47.22	51.80	63.17	69.66
	test	38.14	47.73	56.31	61.94	68.30
mr	train	85.65	87.16	88.04	90.71	92.38
	val	83.58	86.49	87.43	89.96	91.62
	test	83.91	86.41	87.51	88.35	89.98
ohsumed	train	68.90	77.99	82.28	85.28	92.28
	val	59.32	70.54	71.08	75.00	81.16
	test	49.45	65.52	63.29	67.33	72.86
R8	train	97.79	98.16	97.05	97.70	98.80
	val	97.13	98.83	97.78	96.61	97.70
	test	96.48	97.81	97.44	97.76	98.86
R52	train	94.57	97.20	97.06	97.38	98.82
	val	91.32	96.92	96.70	94.62	96.02
	test	87.46	93.89	95.06	95.44	96.85
SST2	train	88.60	91.09	92.78	93.57	95.45
	val	88.77	91.38	92.89	93.49	95.37
	test	90.60	91.74	93.69	94.38	96.28