With the recent growth of the graph-based data, the large graph processing becomes more and more important. In order to explore and to extract knowledge from such data, graph mining methods, like community detection, is a necessity. Although the graph mining is a relatively recent development in the Data Mining domain, it has been studied extensively in different areas (biology, social networks, telecommunications and Internet). The legacy graph processing tools mainly rely on single machine computational capacity, which cannot process large graph with billions of nodes. Therefore, the main challenge of new tools and frameworks lies on the development of new paradigms that are scalable, efficient and flexible. In this paper, we will review the new paradigms of large graph processing and their applications to graph mining domain using the distributed and shared nothing approach used for large data by Internet players. The paper will be organized as a walk through different industrial needs in terms of graph mining passing by the existing solutions. Finally, we will expose a set of open research questions linked with several new business requirements as the graph data warehouse.
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- Large Graph Mining: Recent Developments, Challenges and Potential Solutions
- Copyright Year
- Springer Berlin Heidelberg