{'fraction_non_alphanumeric': 0.015151515151515152, 'fraction_numerical': 0.004662004662004662, 'mean_word_length': 5.607692307692307, '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}}
An evolutionary algorithm for network clustering through traffic matrices

While network clustering is traditionally accomplished just relying on the topology of the network, the new traffic-aware clustering approach employs traffic matrices to take into account the intensity of the relationship between nodes. In the context of traffic-aware clustering we propose a new Evolutionary Clustering algorithm and compare it with the Spectral Filtering algorithm. We compare them using both the Modularity and the Traffic-aware Scaled Coverage metrics, and two real-world datasets, each made of 1000 traffic matrices, respectively from Abilene and Géant networks. Our experiments show that Evolutionary Clustering performs better on all traffic matrices, excepting a minor number of traffic matrices in the Abilene network when the Modularity metric is employed.