情报杂志2023，Vol.42Issue(12)：127-133,7.DOI:10.3969/j.issn.1002-1965.2023.12.019
基于图神经网络异构数据融合的学科新兴主题探测研究
Detecting Scientific Emergency Topic Based on Heterogeneous Data Fusion Using GCN
摘要
Abstract
[Research purpose]Data heterogeneity makes large data integration analysis difficult.Deep fusion learning for data with vari-ous structures aids in improving academic data analysis capability,and support the prediction of scientific emergency topics.[Research method]Two components make up detection analysis:(1)Graph Convolution Network(GCN)for deep fusion with various and hetero-geneous academic data.(2)LSTM model for topic prediction in academic fields.In particular,using deep learning capability of GCN,heterogeneous topics data,including multi-characteristics and co-occurrence relations,are transformed into homogeneous representation vectors,realizing heterogeneous fusion while also providing a unified data base for the subsequent prediction model.In order to anticipate the emergency characteristics of academic topics and provide decision assistance for predicting academic emergency topics,topic represen-tation vectors are then fed into a LSTM model to predict academic emergency characteristics,giving decision assistance for predicting aca-demic emergency topics.[Research conclusion]In the academic discipline of library and information science,the empirical findings sup-port the design of GCN+LSTM model as being reasonable.In addition,the fusion model outperformed than non-fusion models.关键词
学科新兴主题/异构数据/多维特征/共现关系/图卷积神经网络Key words
scientific emerging topic/heterogeneous data/multidimensional features/co-occurrence relations/GCN分类
社会科学引用本文复制引用
段庆锋,陈红,闫绪娴,刘东霞..基于图神经网络异构数据融合的学科新兴主题探测研究[J].情报杂志,2023,42(12):127-133,7.基金项目
教育部人文社会科学项目"基于学术社交媒体的学科新兴趋势识别研究"(编号:20YJA870005)研究成果. （编号:20YJA870005）
