ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Accepted author manuscript, 1.85 MB, PDF document
Important graph mining problems such as Clustering are computationally demanding. To significantly accelerate these problems, we propose ProbGraph: a graph representation that enables simple and fast approximate parallel graph mining with strong theoretical guarantees on work, depth, and result accuracy. The key idea is to represent sets of vertices using probabilistic set representations such as Bloom filters. These representations are much faster to process than the original vertex sets thanks to vectorizability and small size. We use these representations as building blocks in important parallel graph mining algorithms such as Clique Counting or Clustering. When enhanced with ProbGraph, these algorithms significantly outperform tuned parallel exact baselines (up to nearly 50 x on 32 cores) while ensuring accuracy of more than 90% for many input graph datasets. Our novel bounds and algorithms based on probabilistic set representations with desirable statistical properties are of separate interest for the data analytics community. Proofs of theorems & more results: http://arxiv.org/abs/2208.11469
Original language | English |
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Title of host publication | Proceedings of SC 2022 : International Conference for High Performance Computing, Networking, Storage and Analysis |
Publisher | IEEE |
Publication date | 2022 |
Pages | 1-17 |
ISBN (Electronic) | 9781665454445 |
DOIs | |
Publication status | Published - 2022 |
Event | 2022 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2022 - Dallas, United States Duration: 13 Nov 2022 → 18 Nov 2022 |
Conference
Conference | 2022 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2022 |
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Land | United States |
By | Dallas |
Periode | 13/11/2022 → 18/11/2022 |
Sponsor | ACM's Special Interest Group on High Performance Computing (SIGHPC), Association for Computing Machinery, IEEE Computer Society, IEEE's Technical Committee on High Performance Computing (TCHPC) |
Series | International Conference for High Performance Computing, Networking, Storage and Analysis, SC |
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Volume | 2022-November |
ISSN | 2167-4329 |
Bibliographical note
Publisher Copyright:
© 2022 IEEE.
- Approximate Community Detection, Approximate Graph Clustering, Approximate Graph Mining, Approximate Graph Pattern Matching, Approximate Triangle Counting, Bloom Filters, Graph Sketching, High-Performance Graph Computations, K Minimum Values, MinHash
Research areas
ID: 344653134