{'fraction_non_alphanumeric': 0.02580195258019526, 'fraction_numerical': 0.006276150627615063, 'mean_word_length': 4.881147540983607, '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}}
SCAN-XP: Parallel Structural Graph Clustering Algorithm on Intel Xeon Phi Coprocessors

The structural graph clustering method SCAN, proposed by Xu et al., is successfully used in many applications because it not only detects densely connected nodes as clusters but also extracts sparsely connected nodes as hubs or outliers. However, it is difficult to applying SCAN to large-scale graphs since SCAN needs to evaluate the density for all adjacent nodes included in the given graphs. In this paper, so as to address the above problem, we present a novel algorithm SCAN-XP that performs over Intel Xeon Phi. We designed SCAN-XP in order to make best use of the hardware potential of Intel Xeon Phi by employing the following approaches: First, SCAN-XP avoids the bottlenecks that arise from parallel graph computations by providing good load balances among cores on the Intel Xeon Phi. Second, SCAN-XP effectively exploits 512 bit SIMD instructions implemented in the Intel Xeon Phi to speed up the density evaluations. As a result, SCAN-XP detects clusters, hubs, and outliers from large-scale graphs with much shorter computation time than SCAN. Specifically, SCAN-XP runs approximately 100 times faster than SCAN; for the graphs with 100 million edges, SCAN-XP is able to perform in a few seconds. In this paper, extensive evaluations on real-world graphs demonstrate the performance superiority of SCAN-XP over existing approaches.