PegasusN: A Scalable and Versatile Graph Mining System
Abstract
How can we find patterns and anomalies in peta-scale graphs? Even recently proposed graph mining systems fail in processing peta-scale graphs. In this work, we propose PegasusN, a scalable and versatile graph mining system that runs on Hadoop and Spark. To handle enormous graphs, PegasusN provides and seamlessly integrates efficient algorithms for various graph mining operations: graph structure analyses, subgraph enumeration, graph generation, and graph visualization. PegasusN quickly processes extra-large graphs that other systems cannot handle.
Cite
Text
Park et al. "PegasusN: A Scalable and Versatile Graph Mining System." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.11372Markdown
[Park et al. "PegasusN: A Scalable and Versatile Graph Mining System." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/park2018aaai-pegasusn/) doi:10.1609/AAAI.V32I1.11372BibTeX
@inproceedings{park2018aaai-pegasusn,
title = {{PegasusN: A Scalable and Versatile Graph Mining System}},
author = {Park, Ha-Myung and Park, Chiwan and Kang, U},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2018},
pages = {8214-8215},
doi = {10.1609/AAAI.V32I1.11372},
url = {https://mlanthology.org/aaai/2018/park2018aaai-pegasusn/}
}