Mapping flows on weighted and directed networks with incomplete observations

Abstract

Detecting significant community structure in networks with incomplete observations is challenging because the evidence for specific solutions fades away with missing data. For example, recent research shows that flow-based community detection methods can highlight spurious communities in sparse undirected and unweighted networks with missing links. Current Bayesian approaches developed to overcome this problem do not work for incomplete observations in weighted and directed networks that describe network flows. To address this gap, we extend the idea behind the Bayesian estimate of the map equation for unweighted and undirected networks to enable more robust community detection in weighted and directed networks. We derive a weighted and directed prior network that can incorporate metadata information and show how an efficient implementation in the community-detection method Infomap provides more reliable communities even with a significant fraction of data missing.

References in corpus (7)

• Maps of random walks on complex networks reveal community structure
• Finding statistically significant communities in networks
• Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
• Community detection in networks: Structural communities versus ground truth
• Stacking Models for Nearly Optimal Link Prediction in Complex Networks
• Mapping higher-order network flows in memory and multilayer networks with Infomap
• Mapping flows on sparse networks with missing links

Cited by in corpus (1)

• Single-trajectory map equation