冲击力
发表于 2025-3-28 17:08:00
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软膏
发表于 2025-3-28 20:48:57
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命令变成大炮
发表于 2025-3-29 01:50:55
https://doi.org/10.1007/978-3-662-66978-5work, we focus on one of the most relevant topological properties that characterize the network, ..e. being scale-free. A directed network is scale-free if its in-degree and out-degree distributions have an approximate and asymptotic power-law behavior. If we consider the Web as a whole, it presents
Retrieval
发表于 2025-3-29 04:40:05
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闲聊
发表于 2025-3-29 07:34:01
https://doi.org/10.1007/978-3-211-92163-0It is well known that the global clustering coefficient of a standard preferential attachment random graph vanishes as the number of vertices tends to .. We evaluate the global clustering coefficient of the preferred attachment affiliation network [.] and show that it is bounded away from zero.
侵害
发表于 2025-3-29 13:04:32
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使残废
发表于 2025-3-29 17:05:17
Clustering Coefficient of a Preferred Attachment Affiliation Network,It is well known that the global clustering coefficient of a standard preferential attachment random graph vanishes as the number of vertices tends to .. We evaluate the global clustering coefficient of the preferred attachment affiliation network [.] and show that it is bounded away from zero.
编辑才信任
发表于 2025-3-29 20:20:44
Transience Versus Recurrence for Scale-Free Spatial Networks,Weight-dependent random connection graphs are a class of local network models that combine scale-free degree distribution, small-world properties and clustering. In this paper we discuss recurrence or transience of these graphs, features that are relevant for the performance of search and information diffusion algorithms on the network.
RUPT
发表于 2025-3-29 23:54:54
A Note on the Conductance of the Binomial Random Intersection Graph,s bounded away from zero by a positive constant. We consider the range of parameters ., ., . where the edge density is just above the connectivity threshold (our graph is connected with high probability as .). We assume in addition that ..
染色体
发表于 2025-3-30 05:34:10
https://doi.org/10.1007/978-3-030-48478-1graph algorithms; clustering; collaborative filtering; structural properties; routing optimization; visua