RegRole: Regularized Role Detection and Prediction in Temporal Dynamic Networks
2026-08-14 • Social and Information Networks
Social and Information Networks
AI summaryⓘ
The authors developed a method to find consistent roles that people or nodes play over time in changing networks. Their approach uses a mathematical technique called Non-negative Matrix Factorization with added rules to keep role assignments stable between time periods. They tested their method on real and simulated data and found it predicts role changes better and shows fewer sudden, unrealistic changes than other methods. This helps in understanding how behaviors evolve smoothly in complex networks.
Role discoveryTemporal networksNon-negative Matrix FactorizationRegularizationTransition matrixDynamic networksBehavioral transitionsTime-aligned rolesGraph analysisSparse graphs
Authors
Emily J Evans, Weihong Guo, Carlotta Domenicon
Abstract
This paper introduces a dynamic role discovery technique in temporal dynamic networks, utilizing temporally regularized Non-negative Matrix Factorization (NMF). Our technique differs from existing dynamic role analysis techniques by creating a consistent set of roles across all time periods, as well as a universal transition matrix that describes the probability of transitioning between roles. We also apply a regularization penalty to ensure that role membership does not change dramatically between time periods making our model more robust against real-world noise. We test our data on five real-world and one synthetically simulated dataset using both engineered and automatically generated features. We demonstrate that the proposed regularized role detection method, for appropriate regularization weight parameter reduces prediction errors compared to other techniques. Furthermore, trace analysis of the transition matrices indicates that our method yields a more stable system, that is, individuals are more likely to stay in their roles with fewer arbitrary transitions. Our model learns time-aligned roles, captures behavioral transitions over time, and scales efficiently to large and sparse graphs.