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Big Graph Mining: Frameworks and Techniques

Abstract : Big graph mining is an important research area and it has at-tracted considerable attention. It allows to process, analyze,and extract meaningful information from large amounts ofgraph data. Big graph mining has been highly motivated notonly by the tremendously increasing size of graphs but alsoby its huge number of applications. Such applications in-clude bioinformatics, chemoinformatics and social networks.One of the most challenging tasks in big graph mining ispattern mining in big graphs. This task consists on usingdata mining algorithms to discover interesting, unexpectedand useful patterns in large amounts of graph data. It aimsalso to provide deeper understanding of graph data. In thiscontext, several graph processing frameworks and scalingdata mining/pattern mining techniques have been proposedto deal with very big graphs. This paper gives an overviewof existing data mining and graph processing frameworksthat deal with very big graphs. Then it presents a surveyof current researches in the field of data mining / patternmining in big graphs and discusses the main research issuesrelated to this field. It also gives a categorization of bothdistributed data mining and machine learning techniques,graph processing frameworks and large scale pattern miningapproaches.
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Submitted on : Thursday, November 26, 2020 - 2:44:35 PM
Last modification on : Wednesday, November 3, 2021 - 6:41:55 AM

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Sabeur Aridhi, Engelbert Mephu. Big Graph Mining: Frameworks and Techniques. Big Data Research, Elsevier, 2016, 6, pp.1-10. ⟨10.1016/j.bdr.2016.07.002⟩. ⟨hal-03025930⟩



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