A Temporal Network Community Detection Method Based on Steady Labels and Label Propagation SteadyProp (A Temporal Network Community Detection Algorithm with Steady Labels and Label Propagation)

Accurate community detection is crucial for timely comprehension of network structures, understanding evolutionary patterns, and predicting future network behaviors. It greatly enhances applications in social network analysis, disease prediction, and treatment, among others. In temporal community detection algorithms, inert nodes within communities tend to persist within the same community across multiple time slices. In this paper, these nodes are assigned steady-state labels. Identifying and applying steady-state labeled nodes accelerates and smoothens the process of community detection. Thus, this paper introduces a rapid temporal network community detection approach based on steazdy-state labels and label propagation. By leveraging the propagation of steady-state labels at each time step, this method achieves faster temporal community partitioning, effectively enhancing the accuracy and smoothness of such partitions.