{'fraction_non_alphanumeric': 0.01303538175046555, 'fraction_numerical': 0.0, 'mean_word_length': 5.725, '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}}
Discovering Academic Communities and their Research Interests

The paper aims to discover overlapping communities and their research interests in academic social network. The network is constructed based on co-authorships. Cliques are extracted to discover overlapping communities. Keywords used by authors in communities are counted and sorted, and top N keywords are selected to represent their research interests. The experimental results in the field of Information Resource Management testified the effectiveness of proposed method.