Titlebook: Community Structure of Complex Networks; Hua-Wei Shen Book 2013 Springer-Verlag Berlin Heidelberg 2013 Community structure.Complex Network

FOLLY

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

补角

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

Community Structure of Complex Networks978-3-642-31821-4Series ISSN 2190-5053 Series E-ISSN 2190-5061

GLIB

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

Springer Theseshttp://image.papertrans.cn/c/image/230704.jpg

敌意

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

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

nepotism

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.