文献类型：期刊文献

英文题名：Exploring network reliability by predicting link status based on simplex neural network

作者：Huang, Ru[1];Feng, Moran[1];Chen, Zijian[1];He, Jianhua[2];Chu, Xiaoli[3]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Meilong Rd 130, Shanghai 200237, Peoples R China;[2]Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, England;[3]Univ Sheffield, Dept Elect & Elect Engn, Sheffield S1 3JD, England

年份：2023

卷号：79

外文期刊名：DISPLAYS

收录：;EI(收录号：20232514266592);WOS:【SCI-EXPANDED(收录号:WOS:001025995500001)】；

基金：This work was supported by the National Natural Science Foundation of China under Grants 61673178 and 61922063, in part by the Natural Science Foundation of Shanghai, China under Grant 20ZR1413800. Our work was also supported by the Shanghai Key Laboratory Open Project (STCSM 22DZ2229005). The authors would like to thank the anonymous reviewers for their valuable comments and suggestions that help improve the quality of this paper.

语种：英文

外文关键词：Complex networks; Simplicial neural networks; Link prediction; Deep learning; Network analysis

摘要：Complex networks are graph-based structures with non-trivial topological features that frequently occur in real systems. Link prediction plays an important role in various real-world networks application, such as recommendation systems, protein structure prediction, packet forwarding strategy optimization, etc. The existing link prediction approaches mainly focus on superficial heuristic features, while ignoring high-order structure information. In this paper, we propose a deep-learning based model, named Weisfeiler-Lehman Simplex Neural Network (WL-SNN), which can learn the high-order simplex information of the network. In particular, we design a third-order Laplace operator to extract the simplicial features and utilize the graph convolutional network to compensate for the possible deficiencies of the model resulting from the single channel features. Furthermore, we use the Weisfeiler-Lehman algorithm to extract closed subgraphs of the target, which significantly enhances the adaptability of the model to large-scale networks. Experimental results on six real-world networks show that our approach achieves comparable performance in the link prediction task as well as in the stability analysis of the network.