Mapping Flows on Bipartite Networks

TL;DR

This paper enhances community detection in bipartite networks by incorporating node-type information into the map equation, leading to more detailed and accurate flow-based network mappings.

Contribution

It introduces a coding scheme that leverages bipartite structure, improving community detection resolution in flow-based network analysis.

Findings

Using node types increases community hierarchy depth.

Higher node-type information leads to better network regularity detection.

Incorporating bipartite structure improves flow compression.

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.