基于双曲空间图嵌入的科研热点预测

doi:10.11959/j.issn.2096-0271.2022041

Abstract

Abstract:

Scientific topic prediction is central to scientific research and can substantially advance the allocation of scientific resources.Machine learning and data mining approaches have been widely applied to scientific topic prediction, including paper content-based topic model and citation prediction models.A novel scientific topic prediction algorithm PKGM (Poincare keywords graph embedding) was proposed, which utilized keywords and their relations to build a keyword network, and calculated the distance between two nodes in this network to predict the probability that an edge existed.The result of comparing PKGM with seven baselines showed that PKGM obtained a 7.3% improvement by using AUROC and 5.8% improving by using AP in comparison to the best method in Euclidean space, and 10.8% improvement by using AUROC and 7.2% improving by using AP over the best approach in hyperbolic space.The results demonstrated the effectiveness of PKGM.

Key words: scientific topic, hyperbolic space, Poincare's model, graph embedding, keywords network, long tail effect

CLC Number:

TP18

Jun DAI. Emerging scientific topic prediction based on Poincare graph embedding[J]. Big Data Research, 2022, 8(6): 94-104.

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References 33

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实验	测试集/条	训练集/条	验证集/条
实验1	1 897	16 130	949
实验2	5 692	11 386	1 898

算法	实验1				实验2
算法	AUROC	增加	AP	增加	AUROC	增加	AP	增加
PKGM	0.8822	-	0.8906	-	0.8751	-	0.8857	-
Euclidean	0.8180	7.3%	0.8389	5.8%	0.8062	7.9%	0.8276	6.6%
MLP	0.4989	43.4%	0.4995	43.9%	0.5003	42.8%	0.5001	43.5%
GCN	0.7222	18.1%	0.7448	16.4%	0.7406	15.4%	0.7329	17.3%
GAT	0.7590	14.0%	0.7705	13.5%	0.7575	13.4%	0.7674	13.4%

算法	实验1				实验2
算法	AUROC	增加	AP	增加	AUROC	增加	AP	增加
PKGM	0.8822	-	0.8906	-	0.8751	-	0.8857	-
HNN	0.6596	25.2%	0.6466	27.4%	0.6817	22.1%	0.6645	25.0%
HGCN	0.7764	12.0%	0.7930	11.0%	0.7750	11.4%	0.7943	10.3%
HGNN	0.7865	10.8%	0.8264	7.2%	0.7787	11.0%	0.8162	7.8%

Emerging scientific topic prediction based on Poincare graph embedding

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References 33

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