[1] 任晓龙, 吕琳媛. 网络重要节点排序方法综述[J]. 科学通报, 2014, 59(13):1175-1197. REN X L, LÜ L Y. Review of ranking nodes in complex networks[J]. Chinese Science Bulletin, 2014, 59(13):1175-1197. [2] SOLÉ-RIBALTA A, DE DOMENICO M, GÓMEZ S, et al. Centrality rankings in multiplex networks[C]//Proceedings of the 2014 ACM Conference on Web Science. New York:ACM, 2014:149-155. [3] 罗浩, 闫光辉, 张萌, 等. 基于证据理论的多关系网络重要节点挖掘方法[J]. 计算机学报, 2020, 43(12):2398-2413. LUO H, YAN G H, ZHANG M, et al. Identifying important nodes in multi-relational networks based on evidence theory[J]. Chinese Journal of Computers, 2020, 43(12):2398-2413. [4] 罗浩, 闫光辉, 张萌, 等. 融合多元信息的多关系社交网络节点重要性研究[J]. 计算机研究与发展, 2020, 57(5):954-970. LUO H, YAN G H, ZHANG M, et al. Research on node importance fused multi-information for multi-relational social networks[J]. Journal of Computer Research and Development, 2020, 57(5):954-970. [5] DING C F, LI K. Centrality ranking in multiplex networks using topologically biased random walks[J]. Neurocomputing, 2018, 312:263-275. [6] BOCCALETTI S, BIANCONI G, CRIADO R, et al. The structure and dynamics of multilayer networks[J]. Physics Reports, 2014, 544(1):1-122. [7] ZHANG C, SONG D, HUANG C, et al. Heterogeneous graph neural network[C]//Proceedings of the 25th ACM International Conference on Knowledge Discovery & Data Mining. New York:ACM, 2019:793-803. [8] YANG X H, XIONG Z, MA F N, et al. Identifying influential spreaders in complex networks based on network embedding and node local centrality[J/OL]. Physica A:Statistical Mechanics and Its Applications, 2021, 573:125971[2021-09-20]. https://www.sciencedirect.com/science/article/abs/pii/S0378437121002430. [9] KITSAK M, GALLOS L K, HAVLIN S, et al. Identification of influential spreaders in complex networks[J]. Nature Physics, 2010, 6(11):888-893. |