Mapping network flows provides insight into the organization of networks, but even though many real networks are bipartite, no method for mapping flows takes advantage of the bipartite structure. What do we miss by discarding this information and how can we use it to understand the structure of bipartite networks better? The map equation models network flows with a random walk and exploits the information-theoretic duality between compression and finding regularities to detect communities in networks. However, it does not use the fact that random walks in bipartite networks alternate between node types, information worth 1 bit. To make some or all of this information available to the map equation, we developed a coding scheme that remembers node types at different rates. We explored the community landscape of bipartite real-world networks from no node-type information to full node-type information and found that using node types at a higher rate generally leads to deeper community hierarchies and a higher resolution. The corresponding compression of network flows exceeds the amount of extra information provided. Consequently, taking advantage of the bipartite structure increases the resolution and reveals more network regularities.

中文翻译：

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双向网络上的映射流

映射网络流可以洞悉网络的组织，但是即使许多实际网络都是二分的，也没有一种映射流的方法可以利用二分结构。抛弃这些信息，我们会错过什么？如何利用它更好地了解双向网络的结构？映射方程模型以随机游走对网络流进行建模，并利用压缩和寻找规律之间的信息理论对偶性来检测网络中的社区。但是，它没有使用这样的事实，即双向网络中的随机游走会在节点类型之间交替，信息价值为1位。为了使某些或所有这些信息可用于映射方程，我们开发了一种编码方案，可以以不同的速率记住节点类型。我们研究了从没有节点类型信息到完整节点类型信息的两方现实世界网络的社区格局，发现以较高的速率使用节点类型通常会导致更深的社区层次结构和更高的分辨率。网络流的相应压缩超过了所提供的额外信息的数量。因此，利用二分结构可以提高分辨率并揭示更多的网络规律性。