The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Direction-optimizing Breadth-First Search
Abstract
Breadth-First Search is an important kernel used by many graph-processing applications. In many of these emerging applications of BFS, such as analyzing social networks, the input graphs are low-diameter and scale-free. We propose a hybrid approach that is advantageous for low-diameter graphs, which combines a conventional top-down algorithm along with a novel bottom-up algorithm. The bottom-up algorithm can dramatically reduce the number of edges examined, which in turn accelerates the search as a whole. On a multi-socket server, our hybrid approach demonstrates speedups of 3.3 -- 7.8 on a range of standard synthetic graphs and speedups of 2.4 -- 4.6 on graphs from real social networks when compared to a strong baseline. We also typically double the performance of prior leading shared memory (multicore and GPU) implementations.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652
Erdős, P., & Rényi, A. (2022) · Publicationes Mathematicae Debrecen · doi:10.5486/pmd.1959.6.3-4.12 · Cited by 5,337
The university of Florida sparse matrix collection
Davis, T. A., & Hu, Y. (2011) · ACM Transactions on Mathematical Software · doi:10.1145/2049662.2049663 · Cited by 3,630
Pregel: a system for large-scale graph processing
Malewicz, G., Austern, M. H., Bik, A. J. C., Dehnert, J. C., Horn, I., Leiser, N., & Czajkowski, G. (2010) · Proceedings of the 2010 ACM SIGMOD International Conference on Management of data · doi:10.1145/1807167.1807184 · Cited by 3,515
Measurement and analysis of online social networks
Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., & Bhattacharjee, B. (2007) · Proceedings of the 7th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1298306.1298311 · Cited by 3,057
R-MAT: A Recursive Model for Graph Mining
Chakrabarti, D., Zhan, Y., & Faloutsos, C. (2004) · Proceedings of the 2004 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972740.43 · Cited by 1,255
The webgraph framework I: compression techniques
Boldi, P., & Vigna, S. (2004) · Proceedings of the 13th international conference on World Wide Web · doi:10.1145/988672.988752 · Cited by 1,201
User interactions in social networks and their implications
Wilson, C., Boe, B., Sala, A., Puttaswamy, K. P. N., & Zhao, B. Y. (2009) · Proceedings of the 4th ACM European conference on Computer systems · doi:10.1145/1519065.1519089 · Cited by 879
Metadata
- Authors
- Beamer, S., Asanovic, K., & Patterson, D.
- Venue
- 2012 International Conference for High Performance Computing, Networking, Storage and Analysis
- Publisher
- IEEE
- Published
- 2012-11
- Pages
- 1-10
- ISSN
- 2167-4337 (ISSN-L)
- OpenAlex
- W4253426709
- Cited by
- 325
- References
- 21
- FWCI
- 10.03
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Direction-optimizing Breadth-First Search. https://app.cochat.ai/papers/10.1109/sc.2012.50
- The paper
- Beamer, S., Asanovic, K., & Patterson, D. (2012). Direction-optimizing Breadth-First Search. 2012 International Conference for High Performance Computing, Networking, Storage and Analysis. https://doi.org/10.1109/sc.2012.50
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
