UniSKGRep: A unified representation learning framework of social network and knowledge graph

doi:10.1016/j.neunet.2022.11.010

	UniSKGRep: A unified representation learning framework of social network and knowledge graph
	Shen, Yinghan 1,2; Jiang, Xuhui 1,2; Li, Zijian 1,2; Wang, Yuanzhuo 1,3,6; Xu, Chengjin 5; Shen, Huawei 1,2; Cheng, Xueqi 1,2,4
	2023
发表期刊	NEURAL NETWORKS
ISSN	0893-6080
卷号	158 页码:142-153
摘要	The human-oriented applications aim to exploit behaviors of people, which impose challenges on user modeling of integrating social network (SN) with knowledge graph (KG), and jointly analyzing two types of graph data. However, existing graph representation learning methods merely represent one of two graphs alone, and hence are unable to comprehensively consider features of both SN and KG with profiling the correlation between them, resulting in unsatisfied performance in downstream tasks. Considering the diverse gap of features and the difficulty of associating of the two graph data, we introduce a Unified Social Knowledge Graph Representation learning framework (UniSKGRep), with the goal to leverage the multi-view information inherent in the SN and KG for improving the downstream tasks of user modeling. To the best of our knowledge, we are the first to present a unified representation learning framework for SN and KG. Concretely, the SN and KG are organized as the Social Knowledge Graph (SKG), a unified representation of SN and KG. For the representation learning of SKG, first, two separate encoders in the Intra-graph model capture both the social-view and knowledge-view in two embedding spaces, respectively. Then the Inter-graph model is learned to associate the two separate spaces via bridging the semantics of overlapping node pairs. In addition, the overlapping node enhancement module is designed to effectively align two spaces with the consideration of a relatively small number of overlapping nodes. The two spaces are gradually unified by continuously iterating the joint training procedure. Extensive experiments on two real-world SKG datasets have proved the effectiveness of UniSKGRep in yielding general and substantial performance improvement compared with the strong baselines in various downstream tasks.(c) 2022 Elsevier Ltd. All rights reserved.
关键词	Social knowledge graph Graph representation learning Knowledge graph Social network
DOI	10.1016/j.neunet.2022.11.010
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[U1836206] ; National Natural Science Foundation of China[91646120] ; National Natural Science Foundation of China[U21B2046] ; National Natural Science Foundation of China[62172393] ; National Key Research and Development Program of China[2018YFB1402601] ; Zhongyuanyingcai program[204200510002] ; Major Public Welfare Project of Henan Province[201300311200]
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:000892217500012
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/20227
专题	中国科学院计算技术研究所期刊论文
通讯作者	Wang, Yuanzhuo
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Data Intelligent Syst Res Ctr, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China 3.Zhongke Big Data Acad, Zhengzhou, Henan, Peoples R China 4.Inst Comp Technol, Chinese Acad Sci, Key Lab Network data & Sci & Technol, Beijing, Peoples R China 5.Int Digital Econ Acad, Shenzhen, Guangdong, Peoples R China 6.6 Kexueyuan South Rd Zhongguancun, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Shen, Yinghan,Jiang, Xuhui,Li, Zijian,et al. UniSKGRep: A unified representation learning framework of social network and knowledge graph[J]. NEURAL NETWORKS,2023,158:142-153.
APA	Shen, Yinghan.,Jiang, Xuhui.,Li, Zijian.,Wang, Yuanzhuo.,Xu, Chengjin.,...&Cheng, Xueqi.(2023).UniSKGRep: A unified representation learning framework of social network and knowledge graph.NEURAL NETWORKS,158,142-153.
MLA	Shen, Yinghan,et al."UniSKGRep: A unified representation learning framework of social network and knowledge graph".NEURAL NETWORKS 158(2023):142-153.