Counting Short Cycles in Bipartite Graphs: A Fast Technique/Algorithm and a Hardness Result
Bibliographic record
Abstract
In this paper, we propose a new technique, based on the so-called breadth-first search algorithm, to count the short cycles of a bipartite graph. For a general bipartite graph with |V| nodes and girth g, our technique has a time complexity of O(|V|2Δ) to count g-cycles and (g + 2)-cycles, and a time complexity of O(|V|2Δ2) to count (g + 4)-cycles, where Δ is the maximum node degree in the graph. Moreover, for bi-regular bipartite graphs, the latter complexity is further reduced to O(|V|2Δ). Compared to the fastest known algorithm, which has a complexity O(g|V|2Δ2), the proposed method always has a lower complexity for counting g-cycles and (g + 2)-cycles. It also has a lower complexity for counting (g + 4)-cycles in bi-regular graphs and in scenarios where g is increased with the size of the graph. Related to the problem of counting short cycles, we also demonstrate, using a long-standing conjecture, that there is no algorithm with time complexity less than O(|V|2-2/1±i ) that can determine whether a given sparse bipartite graph has a cycle of length 4i. An important application of the results presented here is to count the short cycles of Tanner graphs of low-density parity-check (LDPC) codes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".