OPT: A New Framework for Overlapped and Parallel Triangulation in Large-scale Graphs
Summary: OPT is a disk-based, overlapped, parallel triangulation framework for billion-scale graphs, achieving near-ideal cost by CPU–I/O overlap. It uses internal/external triangles, macro/micro overlaps, and vertex- and edge-iterator models, with linear speedups. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jinha Kim (Pohang University of Science and Technology)
- 2. Wook-Shin Han (Pohang University of Science and Technology)
- 3. Sangyeon Lee (Pohang University of Science and Technology)
- 4. Kyungyeol Park (Pohang University of Science and Technology)
- 5. Hwanjo Yu (Pohang University of Science and Technology)
BibTeX Citation
@inproceedings{kim_sigmod14,
title = {{OPT: A New Framework for Overlapped and Parallel Triangulation in Large-scale Graphs}},
author = {Kim, Jinha and Han, Wook-Shin and Lee, Sangyeon and Park, Kyungyeol and Yu, Hwanjo},
series = {{SIGMOD} '14},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2588555.2588563},
url = {https://dl.acm.org/doi/10.1145/2588555.2588563},
year = {2014}
}Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,092 | Truss-based Community Search: a Truss-equivalence Based Indexing Approach | 2017 | VLDB | 0.00012072382 |
| 2,101 | DUALSIM: Parallel Subgraph Enumeration in a Massive Graph on a Single Machine | 2016 | SIGMOD | 9.0476814e-05 |
| 2,669 | Sliding Window-based Approximate Triangle Counting over Streaming Graphs with Duplicate Edges | 2021 | SIGMOD | 8.1531412e-05 |
| 3,539 | Hypergraph Motifs: Concepts, Algorithms, and Discoveries | 2020 | VLDB | 7.2173698e-05 |
| 4,487 | Approximately Counting Triangles in Large Graph Streams Including Edge Duplicates with a Fixed Memory Usage | 2018 | VLDB | 6.5786974e-05 |
| 4,988 | Ringo: Interactive Graph Analytics on Big-Memory Machines | 2015 | SIGMOD | 6.3251086e-05 |
| 8,907 | On Asymptotic Cost of Triangle Listing in Random Graphs | 2017 | PODS | 5.2559789e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 462 | Counting Triangles in Data Streams | 2006 | PODS | 0.00017807125 |
| 600 | Massive Graph Triangulation | 2013 | SIGMOD | 0.00015740352 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 12,530 | I/O Efficient: Computing SCCs in Massive Graphs | 2013 | SIGMOD |
| 2 | 9,516 | Efficiently Enumerating Minimal Triangulations | 2017 | PODS |
| 3 | 4,285 | TurboGraph++: A Scalable and Fast Graph Analytics System | 2018 | SIGMOD |
| 4 | 462 | Counting Triangles in Data Streams | 2006 | PODS |
| 5 | 9,394 | GraphTwist: Fast Iterative Graph Computation with Two-tier Optimizations | 2015 | VLDB |
| 6 | 3,495 | Accelerating Truss Decomposition on Heterogeneous Processors | 2020 | VLDB |
| 7 | 5,232 | Better Algorithms for Counting Triangles in Data Streams | 2016 | PODS |
| 8 | 265 | On Triangulation-based Dense Neighborhood Graph Discovery | 2011 | VLDB |
| 9 | 3,314 | Accelerating Triangle Counting on GPU | 2021 | SIGMOD |
| 10 | 600 | Massive Graph Triangulation | 2013 | SIGMOD |