Affiliations: [a] School of Computing, SASTRA Deemed University, Thanjavur, India | [b] School of Computer Science and Engineering, University of New South Wales, Sydney, Australia | [c] Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy | [d] Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India
Correspondence:
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Corresponding author: V. Subramaniyaswamy, School of Computing, SASTRA Deemed University, Thanjavur, India. E-mail: vsubramaniyaswamy@gmail.com.
Abstract: Within the graph mining context, frequent subgraph identification plays a key role in retrieving required information or patterns from the huge amount of data in a short period. The problem of finding frequent items in traditional mining changed to the innovation of subgraphs that recurrently occurs in graph datasets containing a single huge graph. Majority of the existing methods target static graphs, and the distributed solution for dynamic graphs has not been explored. But, in modern applications like Facebook, robotics utilizes large evolving graphs. The goal is to design a method to find recurrent subgraphs from a single large evolving graph. In this research paper, a novel approach is proposed called DFSME, which uses SPARK to discover frequent subgraphs from an evolving graph in a distributed environment. DFSME maintains a set of subgraphs between frequent and infrequent subgraphs, which is used to decrease the search space. Our experiments with synthetic and real-world datasets authorize the effectiveness of DFSME for mining of recurrent subgraphs from huge evolving graph datasets.
Keywords: Frequent subgraph, graph mining, big data, evolving graph, SPARK, social network
