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Titlebook: Complex Networks & Their Applications XII; Proceedings of The T Hocine Cherifi,Luis M. Rocha,Murat Donduran Conference proceedings 2024 The

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發(fā)表于 2025-3-21 16:25:20 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Complex Networks & Their Applications XII
副標(biāo)題Proceedings of The T
編輯Hocine Cherifi,Luis M. Rocha,Murat Donduran
視頻videohttp://file.papertrans.cn/232/231488/231488.mp4
概述Presents the latest research in Complex Networks and their Applications.Gathers the edited proceedings of the Twelfth International Workshop on Complex Networks & their Applications.Offers state-of-th
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Complex Networks & Their Applications XII; Proceedings of The T Hocine Cherifi,Luis M. Rocha,Murat Donduran Conference proceedings 2024 The
描述This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the XII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2023). The carefully selected papers cover a wide range of theoretical topics such as network embedding and network geometry; community structure, network dynamics; diffusion, epidemics and spreading processes; machine learning and graph neural networks as well as all the main network applications, including social and political networks; networks in finance and economics; biological networks and technological networks.
出版日期Conference proceedings 2024
關(guān)鍵詞Complex Networks; Complex Networks 2023; Network Models; Network Dynamics; Network Analysis
版次1
doihttps://doi.org/10.1007/978-3-031-53499-7
isbn_softcover978-3-031-53501-7
isbn_ebook978-3-031-53499-7Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Bayesian Hierarchical Network Autocorrelation Models for?Modeling the?Diffusion of?Hospital-Level Quto as social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed. We develop two hierarchical network autocorrelation models to represent peer effects between hospitals when modeling individual outcomes of the patients who attend those
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地板
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Does Isolating High-Modularity Communities Prevent Cascading Failure?y partitions. It seems obvious that isolating high-modularity communities is a good way to prevent the spread of cascading failures. Here we develop a heuristic approach informed by Moore-Shannon network reliability that focuses on dynamics rather than topology. It defines communities directly in te
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發(fā)表于 2025-3-22 08:47:58 | 只看該作者
Two to?Five Truths in?Non-negative Matrix Factorizationent a scaling inspired by the normalized Laplacian (NL) for graphs that can greatly improve the quality of a non-negative matrix factorization. The results parallel those in the spectral graph clustering work of [.], where the authors proved adjacency spectral embedding (ASE) spectral clustering was
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Dual Communities Characterize Structural Patterns and?Robustness in?Leaf Venation Networks analysis can be complemented by the definition of communities in the networks’ plane dual. Such communities are characterized not by weak but by strong connectivity between the different components of the network. We extract dual communities in leaf venation networks, finding that they can reveal s
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Tailoring Benchmark Graphs to?Real-World Networks for?Improved Prediction of?Community Detection Per algorithm and trust the generated results without further investigation, but algorithm performance can vary depending on the network characteristics. We demonstrate that by running experiments on benchmark graphs tailored to match characteristics of a real-world network of interest, a better unders
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Network Based Methodology for?Characterizing Interdisciplinary Expertise in?Emerging Research-world problems. Understanding characteristics of IDR early (as soon as projects get funded), can formatively shape a research community at portfolio, project, and individual investigator levels. This involves surfacing the interacting components and the context that manifest IDR. We present a netwo
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