DAN EMPIRICAL ANALYSIS OF GRAPH ALGORITHM IN CONTEXT OF FREQUENT SUBGRAPH MINING ON GIRAPH SYSTEM
Sadhana Priyadarshini, Sireesha Rodda · Indian Journal of Computer Science and Engineering · 2022
The Recurrent Subgraph Extraction plays a key role in the Graph Mining field when our data is distributed over networks.This paper emphasizes different types of graph mining algorithms with the Giraph Distributed System to get more desirable and valuable results than existing methods.We discuss how our proposed model MapReduce Geometric Multi-way Advanced Optimized Frequent Subgraph Mining (MGMAOFSM) impacts different graph mining mechanisms for centralized and distributed systems.The comparison is done for different criteria such as memory requirement or execution time with real four datasets (Facebook Social Network, Coronavirus (COVID-19) tweets, Google web graph, Patent Citation Network) with different threshold values.We implement various algorithms such as Triangle Closing, Shortest Path, Connected Components, and PageRank algorithms, and find out our proposed algorithm that requires less memory with the Triangle closing algorithm whereas in the case of PageRank is lowest with all threshold values.