DynaMo Analyzation: Dynamic Community Detection by Incrementally Maximizing Modularity Analyzation
Bibliographic record
Abstract
Real-world systems are dynamic and complex. Networks can be used to represent these real-world systems. Cluster formation is very important and challenging problems regarding the networks, especially since the real-world networks are dynamic and hard to distinguish communities. DynaMo, which is dynamic community detection by incrementally maximizing modularity analysis, is one of the algorithms that can cluster the dynamic networks. In this paper, the principle and methodology of DynaMo is discussed to introduce the basic background of DynaMo. In order to test the performance of DynaMo, an experiment is conducted to compare the performance of DynaMo with that of the other two algorithms (LWEP & activation) by using several real-world datasets. After the experiment, we can see that different evaluation criteria results in a different performance of these three algorithms. We can conclude that for the NMI, precision, and purity value part, activation outperforms DynaMo and LWEP, while in the recall and Fl value part, DynaMo outperforms activation and LWEP.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".