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Knowledge Graph Enhanced Heterogeneous Graph Neural Network for Fake News Detection

Open-sourced implementation for Knowledge Enhanced Heterogeneous Graph Neural Network for Fake News Detection - KEHGNN-FD.

Python Dependencies

Our proposed KEHGNN-FD framework is implemented in Python 3.7 and major libraries include:

• Pytorch = 1.11.0+cu102
• [PyG] (https://pytorch-geometric.readthedocs.io/en/latest/) torch-geometric=2.1.0

More dependencies are provided in requirements.txt.

To Run

python src/main.py

Experimental Results

Covid-19	RNN	BiLSTM	TextCNN	TextGCN	AugTextGCN	HGAT	CompareNet	KEHGNN-FD
Accuracy	86.10 $\pm$ 0.31	87.74 $\pm$ 0.13	84.76 $\pm$ 0.09	61.32 $\pm$ 2.03	61.82 $\pm$ 2.56	84.51 $\pm$ 0.05	84.49 $\pm$ 0.10	90.10 $\pm$ 0.26
Precision	85.67 $\pm$ 0.43	93.55 $\pm$ 0.11	88.17 $\pm$ 0.08	50.38 $\pm$ 3.21	50.76 $\pm$ 3.53	84.06 $\pm$ 0.13	84.18 $\pm$ 0.13	89.97 $\pm$ 0.46
Recall	83.46 $\pm$ 0.36	78.33 $\pm$ 0.34	77.17 $\pm$ 0.20	77.22 $\pm$ 3.88	77.58 $\pm$ 3.93	81.23 $\pm$ 0.14	80.82 $\pm$ 0.19	88.36 $\pm$ 1.01
F1-score	84.49 $\pm$ 0.32	85.21 $\pm$ 0.18	82.28 $\pm$ 0.10	58.76 $\pm$ 3.18	59.17 $\pm$ 3.26	82.60 $\pm$ 0.05	82.45 $\pm$ 0.12	89.16 $\pm$ 0.36

FakeNewsNet	RNN	BiLSTM	TextCNN	TextGCN	AugTextGCN	HGAT	CompareNet	KEHGNN-FD
Accuracy	59.39 $\pm$ 0.26	73.12 $\pm$ 0.22	70.49 $\pm$ 0.16	73.34 $\pm$ 1.25	73.55 $\pm$ 1.63	75.51 $\pm$ 0.19	76.55 $\pm$ 0.18	78.73 $\pm$ 2.12
Precision	53.02 $\pm$ 3.81	69.00 $\pm$ 0.32	66.72 $\pm$ 0.11	36.39 $\pm$ 4.46	36.71 $\pm$ 4.56	68.63 $\pm$ 0.21	68.78 $\pm$ 0.22	72.53 $\pm$ 2.78
Recall	4.67 $\pm$ 0.61	64.58 $\pm$ 0.34	67.36 $\pm$ 0.27	30.64 $\pm$ 3.76	31.11 $\pm$ 3.93	75.71 $\pm$ 0.63	79.01 $\pm$ 0.29	80.89 $\pm$ 2.07
F1-score	7.87 $\pm$ 0.95	66.68 $\pm$ 0.29	67.04 $\pm$ 0.14	33.23 $\pm$ 4.07	33.70 $\pm$ 4.13	71.81 $\pm$ 0.26	73.50 $\pm$ 0.18	76.46 $\pm$ 2.08

PAN2020	RNN	BiLSTM	TextCNN	TextGCN	AugTextGCN	HGAT	CompareNet	KEHGNN-FD
Accuracy	52.47 $\pm$ 0.65	52.74 $\pm$ 0.39	50.82 $\pm$ 0.53	55.13 $\pm$ 1.31	55.47 $\pm$ 1.75	63.70 $\pm$ 0.73	63.56 $\pm$ 0.51	71.37 $\pm$ 1.51
Precision	52.70 $\pm$ 0.52	55.41 $\pm$ 0.63	54.32 $\pm$ 0.64	31.77 $\pm$ 3.90	32.21 $\pm$ 4.32	60.81 $\pm$ 0.90	63.61 $\pm$ 0.70	66.73 $\pm$ 1.87
Recall	76.86 $\pm$ 2.85	80.36 $\pm$ 2.86	76.80 $\pm$ 3.51	26.25 $\pm$ 3.24	27.10 $\pm$ 3.70	72.35 $\pm$ 0.78	76.31 $\pm$ 1.30	80.57 $\pm$ 1.81
F1-score	59.82 $\pm$ 1.59	61.41 $\pm$ 1.31	57.74 $\pm$ 1.84	28.62 $\pm$ 3.51	29.15 $\pm$ 3.57	65.82 $\pm$ 0.77	68.28 $\pm$ 0.48	72.97 $\pm$ 1.12

Liar	RNN	BiLSTM	TextCNN	TextGCN	AugTextGCN	HGAT	CompareNet	KEHGNN-FD
Accuracy	54.59 $\pm$ 0.11	60.61 $\pm$ 0.15	58.77 $\pm$ 2.20	54.97 $\pm$ 6.83	55.25 $\pm$ 6.86	56.10 $\pm$ 0.18	54.95 $\pm$ 0.11	59.67 $\pm$ 0.94
Precision	36.31 $\pm$ 2.09	59.47 $\pm$ 0.39	65.87 $\pm$ 3.05	14.7 $\pm$ 24.33	14.9 $\pm$ 22.54	51.36 $\pm$ 0.24	49.81 $\pm$ 0.17	53.73 $\pm$ 1.20
Recall	1.24 $\pm$ 0.15	45.33 $\pm$ 0.58	21.20 $\pm$ 5.41	21.19 $\pm$ 41.7	21.46 $\pm$ 40.5	50.10 $\pm$ 0.25	50.33 $\pm$ 0.19	56.63 $\pm$ 2.50
F1-score	2.34 $\pm$ 0.27	51.12 $\pm$ 0.31	51.06 $\pm$ 3.49	13.84 $\pm$ 24.7	14.33 $\pm$ 25.6	50.71 $\pm$ 0.23	50.03 $\pm$ 0.12	55.09 $\pm$ 0.91