Performance Evaluation of Community Detection Algorithms Based on Relationship Strength Measurement
Bibliographic record
Abstract
The study and analysis of networks have been a significant field of research as it holds its roots in varied disciplines like Biology, Chemistry, Sociology, Computer Applications and many more. Human beings have ventured into the era of a network with the rise of all kinds of networks such as the Internet and Social networks. Detection of community structure in real networks is vital in terms of both theoretical and practical value. Many community detection methods are derived from specific backgrounds and their reliability is still questionable. Researchers hardly focus on the general definition of communities and the general community detection algorithms. The discrepancy between the two might pose some obstacles to the optimization of them so that it is hard to use one algorithm to perfect the other. Recently, Lu et al. [1] have proposed a general method for constructing network model from the real-world problem. Also, they have given a general definition of community structure as well as a complete procedure for detecting communities. In this paper, we apply the efficient resistance distance function [2] on Lu et al.'s algorithm and compare its performance with other community detection algorithms that allow for overlaps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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) | 0.000 | 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".