A Matrix Iteration for Dynamic Network Summaries
A Matrix Iteration for Dynamic Network Summaries
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DOI：
10.1137/110855715
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发表时间：
2013-01-01
期刊：
影响因子：
10.2
通讯作者：
Higham, Desmond J.
中科院分区：
文献类型：
--
作者：
Grindrod, Peter;Higham, Desmond J.
我们提出了一个新的算法来总结大规模时间演化网络的属性。这种类型的数据记录了随着时间推移而出现和消失的连接，在许多现代应用中产生，包括电信和在线人类社会行为。该算法计算一个动态的措施，以及对节点可以通过考虑通过网络的路线，尊重时间的箭头进行通信。我们采用了传统的长度降权方法（消息在沿着传递时会损坏），并添加了年龄降权的新特性（消息会过时）。这使我们能够将广泛使用的Katz式中心性度量推广到在非均匀时间点采样的动态网络的情况，该度量在网络科学中已被证明是流行的。我们说明了合成和真实的数据的新方法。
We propose a new algorithm for summarizing properties of large-scale time-evolving networks. This type of data, recording connections that come and go over time, is generated in many modern applications, including telecommunications and online human social behavior. The algorithm computes a dynamic measure of how well pairs of nodes can communicate by taking account of routes through the network that respect the arrow of time. We take the conventional approach of downweighting for length (messages become corrupted as they are passed along) and add the novel feature of downweighting for age (messages go out of date). This allows us to generalize widely used Katz-style centrality measures that have proved popular in network science to the case of dynamic networks sampled at nonuniform points in time. We illustrate the new approach on synthetic and real data.