{'fraction_non_alphanumeric': 0.016058394160583942, 'fraction_numerical': 0.0, 'mean_word_length': 5.147982062780269, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
On the Effectiveness of the Block Two-Level Erdős-Rényi Generative Network Model

Complex network models have been continuously improved to better match and understand the structural properties and features of real world networks. Such models are useful to generate sample networks with similar characteristics of real world networks that can then be used to improve algorithms and, at the same time, safeguard real data. Several models have been studied and developed over the last few years aiming at matching features like heavy-tailed degree distributions, low diameter and community structure. However, the BTER model is one of a few which was shown capable of generating synthetic networks with all the main characteristics of a real-world network. BTER is capable to match any real degree distribution and clustering coefficient. However, very few experiments have been carried out to support such a claim. In this work, we examine several different parameter setups and then show that the BTER is capable of matching various degree distributions. However, BTER is not capable to correspond to the desired clustering coefficients when restrictions such as connectivity are added to a given network. Further, we also show that the degree distribution plays an important role in the clustering coefficients, independently of how they are parameterized in the model.