Centrality Based Number of Cluster Estimation in Graph Clustering
Bibliographic record
Abstract
Graph clustering algorithms require the number of clusters as an input. However, in many real-world practical applications, the correct number of clusters is unknown. Determining the optimal number of clusters for graph clustering algorithms is an essential and challenging task, which is a form of model order selection. Here, we propose a new algorithm for estimating the number of clusters in a graph using the centrality measure. In graph theory, the centrality measure is used for determining the most important and most influential nodes within a graph. The proposed centrality based number of cluster estimation (CB-NCE) method considers minimizing the probabilistic bounds on the average central error of centrality. The desired criterion represents an information theoretic distance measure in the form of description length of centrality. The simulation results show the superior performance of the proposed algorithm among other existing methods, in terms of clustering performance metrics such as normalized mutual information, Rand index, and F-measure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".