Abstract
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.
Highlights
Many networks are bipartite [1,2,3]
We miss by discarding this node-type information? How can we use it to understand the structure of bipartite networks better?
To explore the value of using bipartite information in community detection, we study the flow-based communitydetection method Infomap [8], which uses an informationtheoretic objective function, known as the map equation [9], to exploit the duality between compression and finding regularities in data
Summary
Many networks are bipartite [1,2,3]. They model interactions between entities of different types, such as users watching movies, documents containing words, and animals eating plants. To explore the value of using bipartite information in community detection, we study the flow-based communitydetection method Infomap [8], which uses an informationtheoretic objective function, known as the map equation [9], to exploit the duality between compression and finding regularities in data. To derive a coding scheme, the map equation uses a hierarchical code that reflects the structure of the network partition. The map equation disregards bipartite information and provides suboptimal compression To address these issues, we developed a coding scheme that uses node-type information at different and adjustable rates. Exploiting node types at higher rates increases the resolution and leads to deeper community structures with more and smaller modules, revealing more network regularities
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