A Map Equation with Metadata: Varying the Role of Attributes in Community Detection
This work addresses the challenge of integrating metadata into network analysis for researchers in fields like social science, but it is incremental as it builds on existing methods with a tuning parameter.
The authors tackled the problem of incorporating metadata into community detection by introducing a tuning parameter to the Infomap algorithm, allowing control over metadata importance; results showed it can overcome structural detectability limits in synthetic networks and achieve greater mutual information with metadata in real-world networks at a cost in the traditional map equation.
Much of the community detection literature studies structural communities, communities defined solely by the connectivity patterns of the network. Often, networks contain additional metadata which can inform community detection such as the grade and gender of students in a high school social network. In this work, we introduce a tuning parameter to the content map equation that allows users of the Infomap community detection algorithm to control the metadata's relative importance for identifying network structure. On synthetic networks, we show that our algorithm can overcome the structural detectability limit when the metadata is well-aligned with community structure. On real-world networks, we show how our algorithm can achieve greater mutual information with the metadata at a cost in the traditional map equation. Our tuning parameter, like the focusing knob of a microscope, allows users to "zoom in" and "zoom out" on communities with varying levels of focus on the metadata.