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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 …