# Big Graph Mining: Frameworks and Techniques

**Authors:** Sabeur Aridhi, Engelbert Mephu Nguifo  
**arXiv:** [1602.03072](https://arxiv.org/abs/1602.03072)  
**Date:** 2016-02-09  
**Summary page:** https://www.summarizepaper.com/en/paper/1602.03072v1/

## Key points

- Big graph mining allows for processing, analysis, and extraction of meaningful information from large amounts of graph data
- Big graph mining has gained significant attention due to its applications in bioinformatics, chemoinformatics, and social networks
- The paper focuses on pattern mining in big graphs as one of the most challenging tasks in big graph mining
- Several graph processing frameworks and scaling data mining/pattern mining techniques have been proposed to address challenges posed by very large graphs
- The authors provide an overview of existing data mining and graph processing frameworks designed for handling big graphs
- They present a survey of current research in the field of data mining/pattern mining in big graphs and discuss main research issues associated with this domain
- Distributed data mining and machine learning techniques, as well as graph processing frameworks and large-scale pattern mining approaches, are categorized to provide a comprehensive understanding of different methods employed in big graph mining
- Aridhi and Nguifo's paper offers valuable insights into the field of big graph mining by providing an overview of existing frameworks and techniques while discussing key research issues

## Summary

Authors Sabeur Aridhi and Engelbert Mephu Nguifo explore the field of big graph mining in their paper "Big Graph Mining: Frameworks and Techniques". They highlight the importance of this research area, which allows for the processing, analysis, and extraction of meaningful information from large amounts of graph data. The authors note that big graph mining has gained significant attention due to its numerous applications in fields such as bioinformatics, chemoinformatics, and social networks as well as the increasing size of graphs. The paper focuses on one of the most challenging tasks in big graph mining: pattern mining in big graphs. This task involves using data mining algorithms to discover interesting patterns within vast amounts of graph data with the aim to provide a deeper understanding of the underlying graph structure. To address these challenges posed by very large graphs, several graph processing frameworks and scaling data mining/pattern mining techniques have been proposed. The authors provide an overview of existing data mining and graph processing frameworks designed specifically for handling big graphs. They also present a survey of current research in the field of data mining/pattern mining in big graphs and discuss the main research issues associated with this domain. Additionally, they categorize distributed data mining and machine learning techniques as well as graph processing frameworks and large-scale pattern mining approaches. This categorization provides a comprehensive understanding of different methods employed to tackle big graph mining challenges. Overall, Aridhi and Nguifo's paper offers valuable insights into the field of big graph mining by providing an overview of existing frameworks and techniques while discussing key research issues. Their work contributes to advancing knowledge in this area and lays a foundation for further exploration into pattern discovery within large-scale graph datasets.