广度学习研究进展:基于情报学的视角

Research Progress of Broad Learning:Based on the Perspective of Informatics

[目的/意义]总结了基于在线社交媒体数据的广度学习工作研究进展,从情报学的视角分析了广度学习的应用展望及未来发展趋势。[方法/过程]利用文献统计分析方法,重点分析了广度学习技术在网络嵌入、链路预测、社区检测等在线社交网络分析领域的应用现状。[结果/结论]广度学习可以将多个不同种类的大型异构数据源融合在一起,设计并使用一套统一的分析方法来跨越这些融合的数据源执行协同数据挖掘任务。广度学习在异构社交网络分析中的这些成功应用为其在情报学领域中的研究奠定了理论基础和技术支持,将会有更广泛更深远的研究成果出现。

[Purpose/significance]This paper summarizes the research progress of broad learning based on online social media data,and puts forward the prospects for broad learning application and development from the perspective of Informatics.[Methods/process]Through the analysis of literature,this paper analyzes the status quo of broad learning application in network embedding,link prediction,community detection and other fields of online social network analysis.[Results/conclusion]Broad learning,which can fuse many different kinds of heterogeneous data sources together,is able to design and use a unified analysis method to perform collaborative data mining tasks across these fused data sources.The successful application of broad learning in online heterogeneous social network analysis has laid a theoretical foundation and technical support for its research in the field of Informatics.