Role-Based Network Embedding via Quantum Walk with Weighted Features Fusion
作者机构：College of Computer ScienceChongqing UniversityChongqing400044China
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2023年第76卷第8期
页 面：2443-2460页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported in part by the National Nature Science Foundation of China(Grant 62172065) the Natural Science Foundation of Chongqing(Grant cstc2020jcyjmsxmX0137)
主 题：Role-based network embedding quantum walk quantum walk weighted characteristic function complex networks
摘 要：Role-based network embedding aims to embed role-similar nodes into a similar embedding space,which is widely used in graph mining tasks such as role classification and *** are sets of nodes in graph networks with similar structural patterns and ***,the rolesimilar nodes may be far away or even disconnected from each ***,the neighborhood node features and noise also affect the result of the role-based network embedding,which are also challenges of current network embedding *** this paper,we propose a Role-based network Embedding via Quantum walk with weighted Features fusion(REQF),which simultaneously considers the influence of global and local role information,node features,and ***,we capture the global role information of nodes via quantum walk based on its superposition property which emphasizes the local role information via biased quantum ***,we utilize the quantum walkweighted characteristic function to extract and fuse features of nodes and their neighborhood by different distributions which contain role information ***,we leverage the Variational Auto-Encoder(VAE)to reduce the effect of *** conduct extensive experiments on seven real-world datasets,and the results show that REQF is more effective at capturing role information in the network,which outperforms the best baseline by up to 14.6% in role classification,and 23% in role detection on average.
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