{'fraction_non_alphanumeric': 0.011120615911035072, 'fraction_numerical': 0.0, 'mean_word_length': 5.25668449197861, '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 Algorithm for Detecting Group in Mobile Social Network

Mobile phone is one of the most prevalent communication tools today with the popularity of mobile devices and wireless technologies. Mobile social network systems are increasingly available. A mobile social network plays an essential role as the spread of information and relationship. This paper propose a new algorithm called group detection algorithm for mining interesting groups in a Campus Mobile Social Network (CMSN) where individuals communicate with one another using mobile phones with short numbers. We use our metric to analyze received and dialed call records within an organization to extract social hierarchy. We analyze the behavior of the communication patterns with taking into account the actual call detail records received and dialed by users. The proposal algorithm is composed of two main components, an algorithm for mining groups and a dynamic programming algo­rithm for selecting subgroups to combine into a big group. Empirical studies on a large real-world campus mobile social network show that performance of our algorithm is an effective algorithm for extracting groups in CMSN.