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)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,135 | Fractal: A General-Purpose Graph Pattern Mining System | 2019 | SIGMOD | 7.4928743e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0019040811 |
| 39 | Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud | 2012 | VLDB | 0.00075263552 |
| 341 | EmptyHeaded: A Relational Engine for Graph Processing | 2016 | SIGMOD | 0.00026850764 |
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