Analysis of Large Graph Partitioning and Frequent Subgraph Mining on Graph Data
Main Article Content
Abstract
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.
Â
Keywords: graph partitioning; frequent subgraph mining; apriori; pattern growth; parallel framework.
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.