Universal Network Generation Model via Exponential Probabilistic Growth and Vari-linear Preferential Attachment

Abstract

Generated networks are widely used in network-based research as a convenient simulation environment. Generating universal networks that more accurately reflect real-world patterns is a cornerstone task. This study proposes a vari-linear network generation model that incorporates two core mechanisms: exponential probabilistic growth and vari-linear preferential attachment. It concurrently overcomes the limitations of traditional growth in characterizing the low-degree region of the degree distribution and the issues regarding the universality of linear preferential attachment. Results indicate that our model describes real-world networks more comprehensively and faithfully, and is highly interpretable. Its performance on diverse empirical datasets is several times better than traditional methods. Related mechanisms and conclusions are substantiated through ablation experiments and statistical analysis. Notably, it achieves a unified interpretation of previously isolated classical network characteristics. This work not only provides a higher-quality universal network generation method, but also bridges the boundaries between traditional concepts, thereby promoting substantive progress in the "world model" of networks.