{'fraction_non_alphanumeric': 0.015122873345935728, 'fraction_numerical': 0.0, 'mean_word_length': 5.086206896551724, '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}}
A Neural Approach to Forming Coherent Teams in Collaboration Networks

We study team formation whose goal is to form a team of experts who collectively cover a set of desirable skills. This problem has mainly been addressed either through graph search techniques, which look for subgraphs that satisfy a set of skill requirements, or through neural architectures that learn a mapping from the skill space to the expert space. An exact graph-based solution to this problem is intractable and its heuristic variants are only able to identify sub-optimal solutions. On the other hand, neural architecture-based solutions treat experts individually without concern for team dynamics. In this paper, we address the task of forming coherent teams and propose a neural approach that maximizes the likelihood of successful collaboration among team members while maximizing the coverage of the required skills by the team. Our extensive experiments show that the proposed approach outperforms the state-of-the-art methods in terms of both ranking and quality metrics.