{'fraction_non_alphanumeric': 0.01926444833625219, 'fraction_numerical': 0.0, 'mean_word_length': 5.5, '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}}
Application of Graph Clustering on Scientific Papers Subject Classification

In this paper, we realize the equivalence between k-means clustering and graph spectrum clustering, and implement a “multi-level algorithm” which combines the advantage of fast computation from k-means and global maximization from spectrum clustering. Then we apply this algorithm on the scientific paper graph which is obtained out of self-defined crawlers over INSPIREHEP databse and perform the clustering. An evaluation is performed and the reason for the imperfect clustering is discussed.