Analysis of community-detection methods based on Potts spin model in complex networks
Bibliographic record
Abstract
Detection of community structures in complex networks is a common challenge in the study of complex networks. Recently, various methods have been proposed to discover community structures at different scales. Here, the multiscale methods based on Potts spin model for community detection are described and compared in the analysis of community structures of several networks. We give a critical analysis of the multiscale methods, showing a kind of limitation that the methods may suffer from when the community size difference is very broad, the breakup of (large) communities will appear before the merger of (small) communities disappears. In particular, we give the explicit expressions for the critical points of the merger and breakup of communities and derive the sufficient conditions (in the form of upper limits) that indicate when the Potts model methods suffer from the limitation. We apply the theoretical results to model networks and show that the method using the configuration null model (i.e., a random graph model as comparison that has the same degree distribution as the network under study) may not recover the full structure of the model network, whereas the method using the Erdös-Rényi null model will do so.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".