{'fraction_non_alphanumeric': 0.033216783216783216, 'fraction_numerical': 0.0, 'mean_word_length': 5.505681818181818, '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}}
Hot or Not ? Finding Star Projects in Coding Social Network

Open-source software and social coding are in demand these days. How would an open-source software stand out and engage more users (development, usage, subscription, etc.) than others? In this paper, we first try to understand the structure of social coding network and then we will attempt to classify popular projects on GitHub using degree centrality, PageRank and logistic regression. Our data are based on GitHub events: ForkEvent, WatchEvent, IssueCommentEvent, PullRequestEvent, PushEvent, ReleaseEvent, IssuesEvent, MemberEvent, and StatusEvent. We will build project-project networks from four of these events (Fork, Watch, Issue Comment and Pull Request) and run logistic regression algorithm on all of these events. This paper will help project contributors to improve their project popularity by focusing on the determining factors and also help users to identify projects that have the potential to become popular later on, so that they can have a wiser choice for their tech stack. Keywords—Github, influential, classification, logistic regression, centrality, PageRank