FOLLY
发表于 2025-3-23 12:45:17
Hillert Ibbeken,Ruprecht Schleyertigate the community structure at a single topological scale. However, community structure of real world networks often exhibits multiple topological descriptions. Furthermore, the detection of multiscale community structure is heavily affected by the heterogeneous distribution of node degree. In th
Hypopnea
发表于 2025-3-23 16:54:23
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补角
发表于 2025-3-23 20:35:48
Source-Synchronous Networks-On-Chipers of each group have similar patterns of connections to other groups. Then, we leverage generative model to describe network structure. The structural regularities are then naturally obtained by statistical inference using expectation maximization algorithm. The most prominent strength of our mode
矿石
发表于 2025-3-23 22:25:15
Community Structure of Complex Networks978-3-642-31821-4Series ISSN 2190-5053 Series E-ISSN 2190-5061
GLIB
发表于 2025-3-24 03:34:55
Hua-Wei ShenNominated by Chinese Academy of Sciences as an outstanding PhD thesis.A comprehensive introduction to community detection in networks.Includes the state-of-the-art development of community detection.P
不妥协
发表于 2025-3-24 06:45:51
Springer Theseshttp://image.papertrans.cn/c/image/230704.jpg
敌意
发表于 2025-3-24 12:49:09
Book 2013ale and coexist with other types of structural regularities of networks. This poses major challenges for conventional methods of community detection. This book will comprehensively introduce the latest advances in community detection, especially the detection of overlapping and hierarchical communit
altruism
发表于 2025-3-24 16:35:50
Hillert Ibbeken,Ruprecht Schleyermetric is proposed to quantify the overlapping community. With this metric, the overlapping community structure can be efficiently detected by directly finding the optimal partition of network using standard modularity. We also describe the applications on word association network and scientific collaboration network.
减弱不好
发表于 2025-3-24 21:36:00
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nepotism
发表于 2025-3-25 00:57:13
Hillert Ibbeken,Ruprecht Schleyer heterogeneity of networks by introducing a rescaling transformation into the covariance matrices in our framework. Extensive tests on real world and artificial networks demonstrate that the proposed method possesses high performance at identifying multiscale community structure in heterogeneous networks.