Modified Lomax model: a heavy-tailed distribution for fitting large-scale real-world complex networks

Real-world networks are generally claimed to be scale-free. This means that the degree distributions follow classical power-law, at least asymptotically. However, closer observation shows power-law distribution is often inadequate meet data characteristics due existence of an identifiable nonlinearity in entire log-log scale. The present paper proposes a new variant popular heavy-tailed Lomax which we named as modified (MLM) can efficiently capture crucial aspect behavior real-world complex networks. proposed MLM model, derived from hierarchical family distributions, fit without removing lower nodes, opposed power-law-based fitting. belongs maximum domain attraction Frechet and right tail equivalent Pareto distribution. Various statistical properties including likelihood estimates asymptotic have also been for model. Finally, effectiveness model demonstrated through rigorous experiments over fifty diverse applied domains.