Bibliographic record
Abstract
Strongly regular graphs are regular graphs with the additional property that the number of common neighbours for two vertices depends only on whether the vertices are adjacent or non-adjacent. From an algebraic point of view, a graph is strongly regular if its adjacency matrix has exactly three eigenvalues. Strongly regular graphs have very interesting algebraic properties due to their strong regularity conditions. Many strongly regular graphs are known to have large and interesting automorphism groups [23]. In [23] it is also conjectured that almost all strongly regular graphs are asymmetric. Peter Cameron in [7] mentions that "Strongly regular graphs lie on the cusp between highly structured and unstructured." Although strongly regular graphs have been studied extensively since they were introduced, there is very little known about the automorphism group of an arbitrary strongly regular graph based on its parameters. In this thesis, we have developed theory for studying the automorphisms of strongly regular graphs. Our study is both mathematical and computational. On the computational side, we introduce the notion of orbit matrices. Using these matrices, we were able to show that some strongly regular graphs do not admit an automorphism of a certain order. Given the size of the automorphism, we can generate all of the orbit matrices, using a computer program. Another computer program is implemented that generates all the strongly regular graphs from that orbit matrix. From a mathematical point of view, we have found an upper bound on the number of fixed points of the automorphisms of a strongly regular graph. This upper bound is a new upper bound and is obtained by algebraic techniques
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".