Heterogeneous Graph Representation Learning Algorithm Based on Attribute Completion

doi:10.12068/j.issn.1005-3026.2025.20240153

Abstract

Abstract:

In the process of collecting heterogeneous graph data， node attributes are often missing due to privacy protection policies or copyright constraints. Regarding both incomplete attributes and completely missing attributes， a heterogeneous graph representation learning algorithm based on attribute completion （HGAC） was proposed. For nodes with incomplete attributes， the missing attributes were obtained by constructing an adjacency matrix in the attribute space and performing graph convolution. Subsequently， the attributes were regarded as abstract nodes， and under the guidance of meta-paths， the topological embeddings of both nodes and attributes were learned. The similarity among the topological embeddings were then used to complete completely missing attributes. Experiments conducted on three real datasets demonstrate that the proposed algorithm effectively enhances the performance of downstream tasks and possesses strong generalization capability.

Key words: graph representation learning, heterogeneous graph, attribute missing, attribute completion, meta-path

CLC Number:

TP 391

Dong-ming CHEN, Jia-ming LIU, Chun-mei LIANG, Dong-qi WANG. Heterogeneous Graph Representation Learning Algorithm Based on Attribute Completion[J]. Journal of Northeastern University(Natural Science), 2025, 46(9): 25-33.

Figures/Tables 4

References 15

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数据集	评价指标	训练比例	Metapath2vec	GCN	HAN	MAGNN	AC-HEN	HGNN-AC	HGAC
ACM	Macro-F1	20	69.43	72.31	90.01	88.01	90.09	90.87	91.49
		40	70.21	73.83	90.82	89.42	90.55	90.97	91.84
		60	70.57	72.59	91.51	90.39	91.22	91.13	92. 21
		80	71.74	72.23	91.71	90.79	91.39	91.52	92.77
	Micro-F1	20	71.13	75.27	89.89	88.08	90.43	90.75	91.63
		40	72.10	76.21	90.73	89.48	90.79	91.02	92.16
		60	72.85	76.10	91.37	90.42	91.23	91.61	92.45
		80	73.24	75.91	91.56	90.80	91.41	92.15	93.05
IMDB	Macro-F1	20	46.42	44.75	57.21	58.11	58.67	58.63	59.53
		40	47.70	45.26	57.54	59.39	59.11	59.39	59.77
		60	48.25	47.70	57.93	59.97	60.17	60.02	60.28
		80	48.73	48.25	58.16	60.02	60.34	60.57	60.84
	Micro-F1	20	48.08	47.44	57.24	58.16	58.66	58.72	59.69
		40	49.55	47.62	57.48	59.46	58.99	59.42	60.11
		60	50.06	48.49	57.68	60.05	59.87	59.84	60.71
		80	50.68	48.73	57.97	60.15	60.03	60.32	61.12
DBLP	Macro-F1	20	90.12	90.06	91.44	92.23	92.29	92.13	93.18
		40	90.74	90.37	91.62	92.87	92.79	92.74	93.73
		60	91.32	90.57	92.01	93.02	93.15	93.27	94.05
		80	91.68	90.74	92.15	93.18	93.22	93.37	94.47
	Micro-F1	20	91.25	90.53	91.89	92.35	92.43	92.58	92.94
		40	91.79	90.83	92.32	93.05	92.96	93.11	93.56
		60	92.33	91.01	92.59	93.38	93.28	93.52	93.98
		80	92.70	91.15	92.68	93.47	93.39	93.58	94.39

数据集	评价指标	训练比例	Metapath2vec	GCN	HAN	MAGNN	AC-HEN	HGNN-AC	HGAC
ACM	Macro-F1	20	69.43	72.31	90.01	88.01	90.09	90.87	91.49
		40	70.21	73.83	90.82	89.42	90.55	90.97	91.84
		60	70.57	72.59	91.51	90.39	91.22	91.13	92. 21
		80	71.74	72.23	91.71	90.79	91.39	91.52	92.77
	Micro-F1	20	71.13	75.27	89.89	88.08	90.43	90.75	91.63
		40	72.10	76.21	90.73	89.48	90.79	91.02	92.16
		60	72.85	76.10	91.37	90.42	91.23	91.61	92.45
		80	73.24	75.91	91.56	90.80	91.41	92.15	93.05
IMDB	Macro-F1	20	46.42	44.75	57.21	58.11	58.67	58.63	59.53
		40	47.70	45.26	57.54	59.39	59.11	59.39	59.77
		60	48.25	47.70	57.93	59.97	60.17	60.02	60.28
		80	48.73	48.25	58.16	60.02	60.34	60.57	60.84
	Micro-F1	20	48.08	47.44	57.24	58.16	58.66	58.72	59.69
		40	49.55	47.62	57.48	59.46	58.99	59.42	60.11
		60	50.06	48.49	57.68	60.05	59.87	59.84	60.71
		80	50.68	48.73	57.97	60.15	60.03	60.32	61.12
DBLP	Macro-F1	20	90.12	90.06	91.44	92.23	92.29	92.13	93.18
		40	90.74	90.37	91.62	92.87	92.79	92.74	93.73
		60	91.32	90.57	92.01	93.02	93.15	93.27	94.05
		80	91.68	90.74	92.15	93.18	93.22	93.37	94.47
	Micro-F1	20	91.25	90.53	91.89	92.35	92.43	92.58	92.94
		40	91.79	90.83	92.32	93.05	92.96	93.11	93.56
		60	92.33	91.01	92.59	93.38	93.28	93.52	93.98
		80	92.70	91.15	92.68	93.47	93.39	93.58	94.39

数据集	评价指标	Metapath2vec	GCN	HAN	MAGNN	AC-HEN	HGNN-AC	HGAC
ACM	NMI	21.22	51.40	61.37	63.17	64.82	64.54	65.22
ACM	ARI	21.00	53.01	64.39	67.41	68.84	68.26	69.75
IMDB	NMI	0.89	7.46	10.62	10.39	11.94	13.02	13.29
IMDB	ARI	0.22	7.69	10.01	11.11	12.17	13.43	13.87
DBLP	NMI	74.23	73.45	77.49	79.64	79.51	79.47	80.69
DBLP	ARI	78.11	77.50	82.95	82.80	84.51	84.66	84.95

数据集	评价指标	Metapath2vec	GCN	HAN	MAGNN	AC-HEN	HGNN-AC	HGAC
ACM	NMI	21.22	51.40	61.37	63.17	64.82	64.54	65.22
ACM	ARI	21.00	53.01	64.39	67.41	68.84	68.26	69.75
IMDB	NMI	0.89	7.46	10.62	10.39	11.94	13.02	13.29
IMDB	ARI	0.22	7.69	10.01	11.11	12.17	13.43	13.87
DBLP	NMI	74.23	73.45	77.49	79.64	79.51	79.47	80.69
DBLP	ARI	78.11	77.50	82.95	82.80	84.51	84.66	84.95