FlexGraph: Flexible partitioning and storage for scalable graph mining

English version

Autoři: Chiwan Park ^aff001; Ha-Myung Park ^aff001; U. Kang ^aff001
Působiště autorů: Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea ^aff001
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0227032

Souhrn

How can we analyze large graphs such as the Web, and social networks with hundreds of billions of vertices and edges? Although many graph mining systems have been proposed to perform various graph mining algorithms on such large graphs, they have difficulties in processing Web-scale graphs due to massive communication and I/O costs caused by communication between workers, and reading subgraphs repeatedly. In this paper, we propose FlexGraph, a scalable distributed graph mining method reducing the costs by exploiting properties of real-world graphs. FlexGraph significantly decreases the communication cost, which is the main bottleneck of distributed systems, by exploiting different edge placement policies based on types of vertices. Furthermore, we propose a flexible storage format to reduce I/O costs when reading input graph repeatedly. Experiments show that FlexGraph succeeds in processing up to 64× larger graphs than existing distributed memory-based graph mining methods, and consistently outperforms previous disk-based graph mining methods.

Klíčová slova:

Algorithms – Data processing – Memory – Network analysis – Social networks – Social systems – Statistical distributions – Twitter

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FlexGraph: Flexible partitioning and storage for scalable graph mining

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