{'fraction_non_alphanumeric': 0.017450682852807285, 'fraction_numerical': 0.0, 'mean_word_length': 5.192488262910798, '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}}
SkyBoundary: An Improved Approach to Member Promotion in Social Networks

With the rapid development of Social Network (SN for short), people increasingly pay attention to the importance of the roles which they play in the SNs. As is usually the case, the standard for measuring the importance of the members is multi-objective. The skyline operator is thus introduced to distinguish the important members from the entire community. For decision-making, people are interested in the most potential stars which can be promoted into the skyline with minimum cost, namely the problem of Member Promotion in Social Networks. In this paper, based on the characteristic of the skyline operator and the promotion process, we first of all propose some interesting new concepts such as Promotion Boundary to design a novel promotion boundary-based pruning strategy. After that, we bring forward an effective cost-based pruning strategy on the basis of permutation and combination theories to verify the plans in the ascending order of cost. The Sky Boundary algorithm is therefore proposed to solve the problem effectively by employing the optimization strategies. Extensive experiments on both real and synthetic datasets are conducted to show the application value, effectiveness and efficiency of the Sky Boundary algorithm.