THS-GWNN:a deep learning framework for temporal network link prediction  

作　　者：Xian MO Jun PANG Zhiming LIU 

机构地区：[1]College of Computer&Information Science,Southwest University,Chongqing 400715,China [2]Faculty of Science,Technology and Medicine&Interdisciplinary Centre for Security,Reliability and Trust,University of Luxembourg,Esch-sur-Alzette L-4364,Luxembourg

出　　处：《Frontiers of Computer Science》2022年第2期174-176,共3页中国计算机科学前沿（英文版）

基　　金：This work has been supported by Chongqing Graduate Student Research and Innovation Project(CYB19096);the China Scholarship Council(202006990041);the Fundamental Research Funds for the Central Universities(XDJK2020D021);the Capacity Development Grant of Southwest University(SWU116007);the National Natural Science Foundation of China(Grant Nos.61672435,61732019,61811530327)。

摘　　要：1 Introduction and main contributions Link prediction for temporal networks aims to evaluate the likelihood of the future linkage among nodes,which has significant applications in social networks,biological networks and traffic analysis[1],etc.Network embedding[2]is an important analytical tool for temporal network link prediction,which helps us better understand network evolution[3].How to encode high-dimensional and non-Euclidean network information is a crucial problem for node embedding in temporal networks.One of the challenges is to reveal the spatial structure at each timestamp and the temporal property over time[4].Some existing work[5]shows that extracting the spatial relation of each node can be used as a valid feature representation for each node.Moreover,the emergence of deep learning techniques[4,5]brings new insights for learning temporal properties,but most models using deep learning still fail to achieve satisfying prediction accuracy.

关 键 词：NETWORK PREDICTION NETWORKS 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]