论文标题
在两部分网络上的映射流
Mapping Flows on Bipartite Networks
论文作者
论文摘要
映射网络流提供了对网络组织的洞察力，但是即使许多实际网络都是两部分，也没有映射流量的方法利用两部分结构。我们通过丢弃这些信息而错过了什么？我们如何使用它更好地理解双方网络的结构？地图方程模型网络以随机步行为流动，并利用压缩和寻找规律性来检测网络中社区的信息理论二元性。但是，它没有使用以下事实：在两分网络中随机步行在节点类型之间交替，信息价值为1位。为了使MAP方程中的某些或全部信息可用，我们开发了一个编码方案，该方案以不同的速率纪念节点类型。我们从没有节点类型的信息到完整的节点型信息，探索了两分现实世界网络的社区格局，并发现以较高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提供的额外信息的数量。因此，利用两分结构会增加分辨率，并揭示更多的网络规律性。
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.
