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Proceedings ArticleDOI

Feature Analysis of Important Nodes in Microblog

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TLDR
The propagation model of the microblog was constructed and the impacts of some nodes with information entropy during the information dissemination, which is so important to guide public opinion and maintain social stability are analyzed.
Abstract
Microblog plays an important role in the dissemination of information now, especially on some sensitive topics. We established the propagation model of the microblog was constructed in this paper. The weak ties are used in the microblog network to obtain independent communities. We analyzed degree centrality, betweenness centrality and closeness centrality of microblog network. Various messages disseminate from different nodes with various feature which can be preset. The characteristics of some nodes in the information dissemination process become clearer according to the comparison among results after message dissemination. We analyzed the impacts of some nodes with information entropy during the information dissemination, which is so important to guide public opinion and maintain social stability.

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References
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Journal ArticleDOI

Collective dynamics of small-world networks

TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Journal ArticleDOI

Emergence of Scaling in Random Networks

TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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Community structure in social and biological networks

TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Journal ArticleDOI

Finding and evaluating community structure in networks.

TL;DR: It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
Journal ArticleDOI

Modularity and community structure in networks

TL;DR: In this article, the modularity of a network is expressed in terms of the eigenvectors of a characteristic matrix for the network, which is then used for community detection.