Centrality measures for networks with community structure
نویسندگان
چکیده
منابع مشابه
Centrality Measures for Networks with Community Structure
Understanding the network structure, and finding out the influential nodes is a challenging issue in the large networks. Identifying the most influential nodes in the network can be useful in many applications like immunization of nodes in case of epidemic spreading, during intentional attacks on complex networks. A lot of research is done to devise centrality measures which could efficiently i...
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Certain real-life networks have a community structure in which communities overlap. For example, a typical bus network includes bus stops (nodes), which belong to one or more bus lines (communities) that often overlap. Clearly, it is important to take this information into account when measuring the centrality of a bus stop—how important it is to the functioning of the network. For example, if ...
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We show that although the prominent centrality measures in network analysis make use of different information about nodes’ positions, they all process that information in a very restrictive and identical way. They all spring from a common family that are characterized by the same axioms. In particular, they are all based on a additively separable and linear treatment of a statistic that capture...
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Numerous centrality measures have been developed to quantify the importances of nodes in time-independent networks, and many of them can be expressed as the leading eigenvector of some matrix. With the increasing availability of network data that changes in time, it is important to extend such eigenvector-based centrality measures to time-dependent networks. In this paper, we introduce a princi...
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There is currently much interest in the problem of measuring the centrality of nodes in networks/graphs; such measures have a range of applications, from social network analysis, to chemistry and biology. In this paper we propose the first measure of node centrality that takes into account the community structure of the underlying network. Our measure builds upon the recent literature on game-t...
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ژورنال
عنوان ژورنال: Physica A: Statistical Mechanics and its Applications
سال: 2016
ISSN: 0378-4371
DOI: 10.1016/j.physa.2016.01.066