标题:Deep Learning Based Link Prediction with Social Pattern and External Attribute Knowledge in Bibliographic Networks
作者:Zhang C.; Zhang H.; Yuan D.; Zhang M.
作者机构:[Zhang, C] Shandong Provincial Key Laboratory of Wireless Communication Technologies, Shandong University, Jinan, 250100, China;[ Zhang, H] Shandong P 更多
来源:Proceedings - 2016 IEEE International Conference on Internet of Things; IEEE Green Computing and Communications; IEEE Cyber, Physical, and Social Computing; IEEE Smart Data, iThings-GreenCom-CPSCom-Smart Data 2016
出版年:2017
页码:815-821
DOI:10.1109/iThings-GreenCom-CPSCom-SmartData.2016.170
摘要:The problem of predicting links for information entities is an important task in network analysis. In this regard, link prediction between authors in bibliographic networks has attracted much attention. However, most of these works only center around exploiting network topology features to do prediction, and other factors affecting link formation are rarely considered. In this paper, we introduce two kinds of novel features based on social pattern and external attribute knowledge (SPEAK), then integrate the SPEAK features and topological features into a deep learning framework using deep neural networks (DNNs). We present the performance based on a real world academic social network from AMiner. Experimental results demonstrate that the SPEAK features can significantly boost the link prediction performance especially when potential links span large geodesic distance. In addition, these features are helpful in understanding the mechanisms behind the link formation. © 2016 IEEE.
收录类别:SCOPUS
Scopus被引频次:2
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020220846&doi=10.1109%2fiThings-GreenCom-CPSCom-SmartData.2016.170&partnerID=40&md5=ac1139d5248db7ccdfa3bd5f20348e54
TOP