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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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书目名称Complex Networks & Their Applications XII
副标题Proceedings of The T
编辑Hocine Cherifi,Luis M. Rocha,Murat Donduran
视频video
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
丛书名称Studies in Computational Intelligence
图书封面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
关键词Complex Networks; Complex Networks 2023; Network Models; Network Dynamics; Network Analysis
版次1
doihttps://doi.org/10.1007/978-3-031-53468-3
isbn_softcover978-3-031-53470-6
isbn_ebook978-3-031-53468-3Series 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
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978-3-031-53470-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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https://doi.org/10.1007/978-3-322-94647-8ex relational data. Large real-world graphs, characterised by sparsity in relations and features, necessitate dedicated tools that existing dense tensor-centred approaches cannot easily provide. To address this need, we introduce a GNNs module in Scikit-network, a Python package for graph analysis,
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https://doi.org/10.1007/978-3-658-16596-3in its early stages. In our research, we repurpose GNN Graph Classification, traditionally rooted in disciplines like biology and chemistry, to delve into the intricacies of time series datasets. We demonstrate how graphs are constructed within individual time series and across multiple datasets, hi
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https://doi.org/10.1007/978-3-658-12285-0such as their .. However, if this were true, modifying only the training procedure for a given architecture would not likely to enhance performance. Contrary to this belief, our paper demonstrates several ways to achieve such improvements. We begin by highlighting the training challenges of GCNs fro
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