Bibliographic record
Abstract
Interconnection networks of various topologies have been widely used in designing multiprocessor architectures. Study of graph theoretical or combinatorial properties of such networks help us better understand them, as well as develop on these architectures more efficient parallel algorithms including fault-tolerant communication/routing algorithms. In this paper, we analyze a broad class of interconnection networks from a new angle by looking into the corresponding graph spectra (i.e., eigenvalues and their multiplicities). Since eigenvalues of the edjacency matrix of a graph can reveal many important properties of the graph that are closely related to its combinatorial invariants, we believe that the study of spectra of interconnection networks can be a more unified approach to studying their topological properties. As a first step) in this direction, here we mainly concentrate on finding out the spectra of some of the most studied interconnection networks. Specifically, after a brief survey of results that relate spectra of graphs to their structural properties, we summarize the existing results for eigenvalues and multiplicities of several popular interconnection networks such as the hypercube and mesh. We also derive some of these results in a more straightforward way. Then we present new results on spectra for some other known networks such as the line graph of the hypercube, followed by experimental results on a few others including the star and pancake networks.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".