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Weighted Graphs and Disconnected Components

机译:加权图和断开连接的组件

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摘要

The vast majority of earlier work has focused on graphswhich are both connected (typically by ignoring all but the giant connected component), and unweighted. Here we study numerous, real, weighted graphs, and report surprising discoveries on the way in which new nodes join and form links in a social network. The motivating questions were the following: How do connected components in a graph form and change over time? What happens after new nodes join a network- how common are repeated edges? We study numerous diverse, real graphs (citation networks, networks in social media, internet traffic, and others);and make the following contributions: (a) we observe that the non-giant connected components seem to stabilize in size, (b) we observe the weights on the edges follow several power laws with surprising exponents, and (c) we propose an intuitive, generative model for graph growth that obeys observed patterns.
机译:早期的绝大多数工作都集中在既相互关联(通常忽略除了巨大的相互联系的分量之外)而且没有加权的图上。在这里,我们研究了许多真实的加权图,并报告了有关新节点在社交网络中加入并形成链接的方式的令人惊讶的发现。激励问题如下:图形中的连接组件如何随时间变化?新节点加入网络后会发生什么—重复边缘有多普遍?我们研究了许多不同的真实图形(引文网络,社交媒体中的网络,互联网流量等);并做出了以下贡献:(a)我们观察到非巨型的连接部分的大小似乎稳定,(b)我们观察到边缘的权重遵循具有令人惊讶指数的几项幂定律,并且(c)我们提出了一种直观的生成模型,用于服从观察到的图形的图形增长。

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