DwarvesGraph: A High-Performance Graph Mining System with Pattern Decomposition

This paper presents DwarvesGraph, the first graph mining system that decomposes the target pattern into several subpatterns, and then computes the count of each. The results of the target pattern can be calculated using the subpattern counts with very low additional cost. Despite decomposition-based algorithms have been studied for years, we propose several novel techniques to address key system challenges: 1) a partial-embedding-centric programming model with efficient supports for pattern existence query and advanced graph mining applications such as FSM; 2) an accurate and efficient cost model based on approximate graph mining; 3) an efficient search method to jointly determine the decomposition of all concrete patterns of an application, considering the computation cost and cross-pattern computation reuse; and 4) the partial symmetry breaking technique to eliminate redundant enumeration for each subpattern while preserving equivalence of computation. Our experiments show that DwarvesGraph is significantly faster than all existing state-of-the-art systems and provides a novel and viable path to scale to large patterns

Twitter and Research: A Systematic Literature Review Through Text Mining

Researchers have collected Twitter data to study a wide range of topics. This growing body of literature, however, has not yet been reviewed systematically to synthesize Twitter-related papers. The existing literature review papers have been limited by constraints of traditional methods to manually select and analyze samples of topically related papers. The goals of this retrospective study are to identify dominant topics of Twitter-based research, summarize the temporal trend of topics, and interpret the evolution of topics withing the last ten years. This study systematically mines a large number of Twitter-based studies to characterize the relevant literature by an efficient and effective approach. This study collected relevant papers from three databases and applied text mining and trend analysis to detect semantic patterns and explore the yearly development of research themes across a decade. We found 38 topics in more than 18,000 manuscripts published between 2006 and 2019. By quantifying temporal trends, this study found that while 23.7% of topics did not show a significant trend ( P=\u3e0.05 ), 21% of topics had increasing trends and 55.3% of topics had decreasing trends that these hot and cold topics represent three categories: application, methodology, and technology. The contributions of this paper can be utilized in the growing field of Twitter-based research and are beneficial to researchers, educators, and publishers