A systematic approach to classifying and evaluating heterogeneity measures

This study introduces a systematic framework for analysing heterogeneity through three principal measure classes: dispersion-based, expected-difference and divergent approaches. I demonstrate that these classes capture distinct structural aspects, with graph heterogeneity measures incorporating global topology beyond degree counts while degree-focused approaches quantify connectivity variation. Key findings establish that apparent inconsistencies across measures reflect heterogeneity's complex nature rather than methodological flaws. The framework enables context-appropriate measure selection for applications ranging from epidemiological modelling to cyber security, while highlighting the critical distinction between degree-focused and topology-aware heterogeneity quantification. The work advances network science by mapping methodological trade-offs and proposing future development of tunable hybrid measures for complex systems analysis.