完整加权网络中的社区检测 [英] Community Detection in complete and weighted networks
问题描述
我确实有一个完整的网络图，其中每个顶点相互连接，并且它们的权重形式不同.一个示例网络是:一个贸易网络，其中每个国家都以某种方式彼此连接，并且仅在不同的贸易形式上有所不同.
I do have a complete network graph where every vertex is connected with each other and they only differ in form of their different weights. A example network would be: a trade network, where every country is connected with each other somehow and only differ in form of different trading volumina.
现在的问题是我如何才能以这种网络形式执行社区检测.通常的可疑对象(算法)只能在未加权或不完整的网络中执行良好.主要问题是测地线到处都是一样的.
Now the question is how I could perform a community detection in that form of network. The usual suspects (algorithm) are only able to perform in either unweighted or incomplete networks well. The main problem is that the geodesic is everywhere the same.
我想到了两种选择:
- 通过将网络切成一定的权重阈值"水平，将网络切成小块
- 或者使用层次聚类算法将整个网络变成一个块模型.但我认为大地测量学无差异"问题仍然存在.
推荐答案
建议了几种方法.
大型网络中社区的快速展开中提出了一种简单而有效的方法(Blondel等人.，2008).它支持加权网络.引用摘要:
One simple yet effective method was suggested in Fast unfolding of communities in large networks (Blondel et al., 2008). It supports weighted networks. Quoting from the abstract:
我们提出了一种提取大型社区结构的简单方法 网络.我们的方法是一种基于模块化的启发式方法 优化.它的表现优于所有其他知名社区 计算时间方面的检测方法.而且，质量 根据所谓的衡量，所检测到的社区中有非常好的 模块化.
We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection method in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity.
引文:
我们现在介绍发现高模块化分区的算法 在短时间内建立大型网络，这将展示一个完整的网络 网络的分层社区结构，从而提供 获得不同的社区检测解决方案.
We now introduce our algorithm that finds high modularity partitions of large networks in short time and that unfolds a complete hierarchical community structure for the network, thereby giving access to different resolutions of community detection.
因此对于完整的图形来说，它应该可以很好地工作，但是您最好检查一下.
So it supposed to work well for complete graph, but you should better check it.
此处(现在已维护您的其他想法-使用权重阈值-可能被证明是一个很好的预处理步骤，尤其是对于不会划分完整图形的算法.我认为最好将其设置为权重的某个百分位(例如中位数).
Your other idea - using weight-threshold - may prove as a good pre-processing step, especially for algorithms which won't partition complete graphs. I believe it is best to set it to some percentile (e.g. to the median) of the weights.
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