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Full-Text Articles in Physical Sciences and Mathematics
Scalable Algorithm Design And Performance Analysis For Graph Motifs Discovery, Md Abdul Motaleb Faysal
Scalable Algorithm Design And Performance Analysis For Graph Motifs Discovery, Md Abdul Motaleb Faysal
UNLV Theses, Dissertations, Professional Papers, and Capstones
Discovering motifs or structural patterns, such as communities, is a significant graph application utilized for classifying groups in social and business networks, identifying similar proteins, detecting anomalous behavior in the cybersecurity domain, and finding critical entities in rumor propagation or infectious disease spreading. Existing state-of-the-art techniques for community discovery face challenges related to scalability, performance limitations, and methodological inaccuracies. The objective of this doctoral dissertation is to introduce novel parallel algorithms and propose high-performance computing architecture designs to address the performance constraints of current community detection approaches when processing large-scale social and biological data. This research focuses on two main …
Parallel Algorithms For Scalable Graph Mining: Applications On Big Data And Machine Learning, Naw Safrin Sattar
Parallel Algorithms For Scalable Graph Mining: Applications On Big Data And Machine Learning, Naw Safrin Sattar
University of New Orleans Theses and Dissertations
Parallel computing plays a crucial role in processing large-scale graph data. Complex network analysis is an exciting area of research for many applications in different scientific domains e.g., sociology, biology, online media, recommendation systems and many more. Graph mining is an area of interest with diverse problems from different domains of our daily life. Due to the advancement of data and computing technologies, graph data is growing at an enormous rate, for example, the number of links in social networks is growing every millisecond. Machine/Deep learning plays a significant role for technological accomplishments to work with big data in modern …
Scalable Community Detection Using Distributed Louvain Algorithm, Naw Safrin Sattar
Scalable Community Detection Using Distributed Louvain Algorithm, Naw Safrin Sattar
University of New Orleans Theses and Dissertations
Community detection (or clustering) in large-scale graph is an important problem in graph mining. Communities reveal interesting characteristics of a network. Louvain is an efficient sequential algorithm but fails to scale emerging large-scale data. Developing distributed-memory parallel algorithms is challenging because of inter-process communication and load-balancing issues. In this work, we design a shared memory-based algorithm using OpenMP, which shows a 4-fold speedup but is limited to available physical cores. Our second algorithm is an MPI-based parallel algorithm that scales to a moderate number of processors. We also implement a hybrid algorithm combining both. Finally, we incorporate dynamic load-balancing in …
Implementation Techniques For The Truncated Fourier Transform, Li Zhang
Implementation Techniques For The Truncated Fourier Transform, Li Zhang
Electronic Thesis and Dissertation Repository
We study various algorithms for the Truncated Fourier Transform (TFT) which is a variation of the Discrete Fourier Transform (DFT) that allows one to work with an input vector of arbitrary size without zero padding. After a review of the original algorithms for the forward and inverse TFT introduced by J. van der Hoeven, we consider the variation of D. Harvey as well as that of J. Johnson and L.C. Meng. Both variations are based on Cooley-Tukey like formulas. The former is called strict general radix as it strictly follows the specifications proposed by J. van der Hoeven, while the …