作者: 馬具 時間: 2025-3-21 22:10
Studies in Computational Intelligencehttp://image.papertrans.cn/c/image/231496.jpg作者: observatory 時間: 2025-3-22 00:54 作者: 諂媚于性 時間: 2025-3-22 08:14 作者: DEAF 時間: 2025-3-22 11:37 作者: 娘娘腔 時間: 2025-3-22 16:04 作者: 娘娘腔 時間: 2025-3-22 18:52
https://doi.org/10.1007/978-3-031-44274-2orithm 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作者: Presbyopia 時間: 2025-3-22 21:20 作者: Peristalsis 時間: 2025-3-23 01:27 作者: 薄膜 時間: 2025-3-23 06:28
Ezra N. Hoch,Danny Bickson,Danny Dolevre, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i.e., Normalized Mutual Information). In this paper we propose a novel approach aimed at evaluating th作者: 長矛 時間: 2025-3-23 12:19 作者: 熱情贊揚 時間: 2025-3-23 15:33 作者: cognizant 時間: 2025-3-23 18:11 作者: 性學院 時間: 2025-3-24 00:28 作者: entice 時間: 2025-3-24 05:24
978-3-319-80839-0Springer International Publishing Switzerland 2016作者: boisterous 時間: 2025-3-24 07:37
Complex Networks VII978-3-319-30569-1Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: 藥物 時間: 2025-3-24 14:38
Stabilization, Optimal and Robust Controlthe degrees of their adjacent nodes dominates the change in network controllability. In our surveyed real networks, this correlation is positive, meaning the number of controls increases quickly when weaker links are targeted first. We confirm this result with synthetic networks from both the scale-作者: Munificent 時間: 2025-3-24 16:27 作者: 財主 時間: 2025-3-24 22:41 作者: 木質 時間: 2025-3-24 23:20 作者: AVID 時間: 2025-3-25 05:40
Temporal Multi-layer Network Construction from Major News Events作者: 賄賂 時間: 2025-3-25 11:30
1860-949X s, (2)Multilayer networks, (3) Controllability of networks, (4) Algorithms fornetworks, (5) Community detection, (6) Dynamics and spreading phenomena onnetworks, (7) Applications of Networks..978-3-319-80839-0978-3-319-30569-1Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: Increment 時間: 2025-3-25 13:46
Sensitivity of Network Controllability to?Weight-Based Edge Thresholdingthe degrees of their adjacent nodes dominates the change in network controllability. In our surveyed real networks, this correlation is positive, meaning the number of controls increases quickly when weaker links are targeted first. We confirm this result with synthetic networks from both the scale-作者: 極深 時間: 2025-3-25 16:20
Particle Filtering as a Modeling Tool for Anomaly Detection in Networksn a hidden markov model, we can track and identify the potential abnormal clusters. We study the performances of this system by analyzing the false alarm rate vs detection rate trade-off by means of Receiver Operating Characteristic curve, and compare the results with the Kalman filter. We validate 作者: intangibility 時間: 2025-3-25 22:15 作者: Defraud 時間: 2025-3-26 02:02 作者: 作嘔 時間: 2025-3-26 05:14
A Novel Approach to Evaluate Community Detection Algorithms on Ground Truthute on large-scale networks. We evaluate its correctness by applying it to six popular community detection algorithms on four large-scale network datasets. Experimental results show how our approach allows to easily evaluate the obtained communities on the ground truth and to characterize the quality of community detection algorithms.作者: Bucket 時間: 2025-3-26 11:19 作者: interference 時間: 2025-3-26 13:07 作者: 亞麻制品 時間: 2025-3-26 17:19 作者: 到婚嫁年齡 時間: 2025-3-26 22:53
https://doi.org/10.1007/978-3-642-05118-0ther important finding is that not every metric performs equally well on all networks. We observe that performance of link prediction ranking is correlated with certain network properties, such as the network size or average node degree.作者: 災難 時間: 2025-3-27 05:08
Jiajia Zhao,Lili Su,Nancy Lynchn dominant orbits indicates that the player himself is dominant. Even in a very sparse network and without any background knowledge on the tournaments or stages of the matches, our proposal is able to extract meaningful rankings which capture the intricate competitive relationships between players from different eras.作者: Acumen 時間: 2025-3-27 08:09
From Self- to Snap- Stabilizationopulation level, the distribution of both profiles agrees with empirical observations in human mobility. Finally, we create a network representation of the most popular websites from the aggregated browsing trajectories and uncover many functional clusters related with different users’ activities.作者: 加強防衛(wèi) 時間: 2025-3-27 12:34 作者: Chandelier 時間: 2025-3-27 14:22
The Marginal Benefit of Monitor Placement on Networkstperforms them in the beginning of the inference. Finally, a website was created where these algorithms can be tested live on preloaded networks or custom networks as desired by the user. The visualization also displays the network as it is being inferred, and provides other statistics about the real and inferred networks.作者: enumaerate 時間: 2025-3-27 18:44 作者: 乳白光 時間: 2025-3-28 01:38 作者: Allodynia 時間: 2025-3-28 03:19 作者: 鋼筆尖 時間: 2025-3-28 08:34 作者: ORE 時間: 2025-3-28 13:57
1860-949X on the interdisciplinary nature of complex-network approach.The lastdecades have seen the emergence of Complex Networks as the language with whicha wide range of complex phenomena in fields as diverse as Physics, ComputerScience, and Medicine (to name just a few) can be properly described andunders作者: 安撫 時間: 2025-3-28 17:50 作者: 跟隨 時間: 2025-3-28 18:50 作者: 使激動 時間: 2025-3-29 00:15 作者: galley 時間: 2025-3-29 04:32 作者: 防銹 時間: 2025-3-29 10:52
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, 作者: NOMAD 時間: 2025-3-29 14:16
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作者: 該得 時間: 2025-3-29 18:19 作者: Osteoporosis 時間: 2025-3-29 20:25
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作者: magnate 時間: 2025-3-30 02:57
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作者: mitral-valve 時間: 2025-3-30 04:04 作者: 發(fā)現(xiàn) 時間: 2025-3-30 12:07 作者: 真 時間: 2025-3-30 13:28
