{'fraction_non_alphanumeric': 0.009900990099009901, 'fraction_numerical': 0.0, 'mean_word_length': 5.785234899328859, '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}}
The community detection algorithm based on the node clustering coefficient and the edge clustering coefficient

Community detection in complex networks can explore the information hidden in the exterior data relationships, understand the internal structure and function of complex system, and improve the efficiency of the system. So community detection has a high practical value. This paper adopts the idea of agglomerative method, proposes the concept of clustering centrality as the standard of selecting community centers' and agglomerate communities based on local information parameters including of node clustering coefficient and edge clustering coefficient. It can reduce the computational complexity. This paper adjusts the initial community partition based on the difference between indegree and outdegree of communities in order to facilitate the global optimization. So that the final result is closer to actual community structure and this algorithm improves the accuracy of community detection.