{'fraction_non_alphanumeric': 0.017894736842105262, 'fraction_numerical': 0.0, 'mean_word_length': 6.097014925373134, '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}}
Sequential Pattern Mining of Social Networks

A social network is a graph made of social entities such as individuals, corporations, collective social units and organizations linked by interdependencies such as kinships, friendships, common interests, co-authorships, beliefs, prestiges or financial exchanges. Social network analysis and minings search implicit, previously unknown and potentially useful relationships from social networks. Most of the data mining techniques concentrate on finding information from data, or predicting future outcomes, but they provide little or no knowledge of how users of these data are connected to each other. This survey reviews research on various methods of subgraph/sequential pattern mining to identify interesting frequently occurring interactions by detecting social communities through hierarchical clustering, their trend analysis and the visual representation to present the mined social information.