Bond Additive Molecular Descriptors of Interconnection Networks
Bibliographic record
Abstract
In this article, a graph-theoretical framework is employed to analyze the structural characteristics of key interconnection networks -butterfly, benes, and mesh-derived networks (MDNs): MDN1 and MDN2, through the application of bond-additive molecular descriptors, particularly the Adriatic indices.The aim is to quantitatively assess the efficiency and strength of these networks using tools from chemical graph theory.Each network is represented as a graph, and various forms of Adriatic indices are computed analytically, incorporating different edge-weighting schemes.These indices effectively characterize topological features such as connectivity, regularity, redundancy, and fault tolerance.The findings indicate that butterfly and benes networks exhibit high regularity with limited redundancy, whereas MDNs demonstrate enhanced fault tolerance and scalability.This consistent descriptor-based analysis facilitates comparative evaluation of network architectures across different sizes and complexities.The approach introduced in this study bridges molecular descriptor theory with interconnection network analysis, offering both theoretical and practical insights.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".