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Graph Data Mining with Arabesque

Summary: Arabesque offers a dedicated framework for graph data mining (subgraph enumeration, motifs) with a simple programming model. It scales to billions of subgraphs on hundreds of cores, addressing limitations of general graph analytics; demonstration highlights end-user experience. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5438
Venue
SIGMOD
Year
2017
Pagerank
5.093636e-05
Overall Rank
11,988 | 17.76%
DOI
10.1145/3035918.3058742

Incoming Non-self Citations Over Time

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Authors

BibTeX Citation

@inproceedings{hussein_sigmod17,
        title = {{Graph Data Mining with Arabesque}},
        author = {Hussein, Eslam and Ghanem, Abdurrahman and Dias, Vinicius Vitor dos Santos and Teixeira, Carlos H. C. and AbuOda, Ghadeer and Serafini, Marco and Siganos, Georgos and Morales, Gianmarco De Francisci and Aboulnaga, Ashraf and Zaki, Mohammed},
        series = {{SIGMOD} '17},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3035918.3058742},
        url = {https://dl.acm.org/doi/10.1145/3035918.3058742},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
2,522 Fractal: A General-Purpose Graph Pattern Mining System 2019 SIGMOD 8.4713567e-05
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