flaggy 发表于 2025-3-26 22:13:20
http://reply.papertrans.cn/24/2315/231485/231485_31.png自作多情 发表于 2025-3-27 04:06:25
http://reply.papertrans.cn/24/2315/231485/231485_32.pngstroke 发表于 2025-3-27 08:36:00
https://doi.org/10.1007/BFb0039570ity of centrality measures is severely affected by missing nodes. This paper investigates the reliability of centrality measures when missing nodes are likely to belong to the same community. We study the behavior of five commonly used centrality measures in uniform and scale-free networks in varioustroke 发表于 2025-3-27 10:42:05
https://doi.org/10.1007/BFb0039570raph model allowing for overlapping community structure. We present the new algorithm . (SPOC) which combines the ideas of spectral clustering and geometric approach for separable non-negative matrix factorization. The proposed algorithm is provably consistent under MMSB with general conditions on t无动于衷 发表于 2025-3-27 14:47:14
http://reply.papertrans.cn/24/2315/231485/231485_35.pngVisual-Acuity 发表于 2025-3-27 21:04:35
https://doi.org/10.1007/BFb0039570ing methods have been used for network equivalence to analyze the power transactions across the interconnections. However, the GSF-based methods are sensitive to location changes of slack bus since GSFs depend on the location of slack bus, which may increase the complexity of market analysis. In thimeditation 发表于 2025-3-27 23:35:22
http://reply.papertrans.cn/24/2315/231485/231485_37.pngshrill 发表于 2025-3-28 05:02:56
http://reply.papertrans.cn/24/2315/231485/231485_38.png杂色 发表于 2025-3-28 07:32:44
https://doi.org/10.1007/BFb0039570 to address. In this paper, we investigate the problem of link prediction in the multilayer scientific collaboration network. Our proposed solution alters the classic stacking technique for the supervised link prediction in terms of distribution of the training and testing data according to the stru不在灌木丛中 发表于 2025-3-28 13:36:19
Additive-Quadratic Functional Equations,ks) change temporally. In regards to time-evolving model in social network analyses, link prediction supports the understanding of the rationale behind the underlying growth mechanisms of social networks. Mining the temporal patterns of actor-level evolutionary changes in regards to their network ne