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Titlebook: Complex Networks VII; Proceedings of the 7 Hocine Cherifi,Bruno Gon?alves,Roberta Sinatra Conference proceedings 2016 Springer Internationa

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41#
發(fā)表于 2025-3-28 17:50:42 | 只看該作者
42#
發(fā)表于 2025-3-28 18:50:01 | 只看該作者
43#
發(fā)表于 2025-3-29 00:15:31 | 只看該作者
44#
發(fā)表于 2025-3-29 04:32:59 | 只看該作者
45#
發(fā)表于 2025-3-29 10:52:47 | 只看該作者
Spanning Edge Betweenness in Practice of an edge being part of a minimum spanning tree. This probability reflects how redundant an edge is in what concerns the connectivity of a given network and, hence, its value gives information about the network topology. We apply this metric to distinct empirical networks and random graph models,
46#
發(fā)表于 2025-3-29 14:16:58 | 只看該作者
Sensitivity of Network Controllability to?Weight-Based Edge Thresholdings edges of a network are removed according to their edge weight. A significant challenge to analyzing real-world networks is that surveys to capture network structure are almost always incomplete. While strong connections may be easy to detect, weak interactions, modeled by small edge weights are th
47#
發(fā)表于 2025-3-29 18:19:40 | 只看該作者
48#
發(fā)表于 2025-3-29 20:25:44 | 只看該作者
Particle Filtering as a Modeling Tool for Anomaly Detection in Networksmmunication networks. However, this assumption done with a strong evidence is not generally proved in a rigorous way. So it is important to develop other methodology, for the scope of anomaly detection, which are not obliged to be based on that assumption. This paper is focused on the use of particl
49#
發(fā)表于 2025-3-30 02:57:46 | 只看該作者
The Marginal Benefit of Monitor Placement on Networksorithm to infer nodes and edges in an unknown network. Our algorithm utilizes monitors that detect incident edges and adjacent nodes with their labels and degrees. The algorithm infers the network through a preferential random walk with a probabilistic restart at a previously discovered but unmonito
50#
發(fā)表于 2025-3-30 04:04:51 | 只看該作者
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