BENCHMARKING THE INFLUENTIAL NODES IN COMPLEX NETWORKS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Among diverse topics in complex network analysis, the idea of extracting a small set of nodes which can maximally influence other nodes in the network has a variety of applications, especially for e-marketing and social networking. While there is an abundance of heuristics to identify such influential nodes, the method of quantifying the influence itself, has not been investigated in the research community. Most of the classical and state-of-the-art works use Diffusion tests for influence benchmark of a particular set of nodes in the network. The underlying study challenges this method and conducts thorough experiments to show that for real-world applications, the diffusion test alone is not only insufficient, but in some cases is also an inaccurate method of benchmarking. Using eight widely adopted heuristics, 25 networks were tested using Diffusion tests and compared with resilience test, we found out that no single algorithm performs consistently on both types of tests. Thus, we conclude that a more accurate way of benchmarking a set of influential nodes is to run diffusion tests alongside resilience test, in order to label a certain technique as best performer.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it