Analysis of Large Graph Partitioning and Frequent Subgraph Mining on Graph Data

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Appala Srinuvasu Muttipati
Dr. Poosapati Padmaja


Graph mining has attracted much attention due to explosive growth in generating graph databases. The graph database is one type of database that consists of either a single large graph or a number of relatively small graphs. Some applications that produce graph database are biological networks, semantic web and behavioural modelling. Frequent subgraph mining is playing an essential role in data mining, with an objective of extracting knowledge in the form of repeated structures. Many efficient subgraph mining algorithms have been discovered in the last two decades, yet most do not scale to the type of data, the so-called “Large-Scale Graph Dataâ€. Many problems are so large or complex that it is impractical or impossible to solve them on a single computer, especially with given limited memory. Scalable parallel computing algorithms holds the key role for solving the problem in this context. Various algorithms and parallel frameworks have been discussed for graph partitioning, frequent subgraph mining based on apriori and pattern growth approaches, and large-scale graph processing techniques. The central objective of this paper is to initiate research and development of identifying frequent subgraph mining and strategies for graph data centres in such a way that brings it parallel frameworks for achieving memory scalability, partitioning, load balancing, granularity, and technical enhancement for future generations.


Keywords: graph partitioning; frequent subgraph mining; apriori; pattern growth; parallel framework.


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