Hybrid Centrality and Temporal Dynamics Framework for Identifying Influential Nodes and Predicting Links in Complex Networks
Bibliographic record
Abstract
For applications like recommendation systems, information diffusion, and epidemic control, it is essential to identify influential nodes in complex networks. Conventional centrality measures use global structures, which are more computationally expensive, or local structures, which are less accurate. For link prediction, recent developments have suggested ensemble learning techniques, mixed and global isolating centralities, and more. Three essential elements are integrated in the Hybrid Centrality Temporal Dynamics (HCTD) framework. Initially, it presents a Hybrid Centrality Score (HCS) that blends Global Isolating Centrality (ISCD2) and Mixed Centrality (MC). Second, it uses modeling of temporal dynamics to monitor the evolution of influence over time. Third, it uses link prediction based on machine learning to foresee upcoming significant interactions. A variety of datasets, such as social media, transportation, and biological networks, are used to validate the framework. Experiments with SIR simulations and ensemble prediction models demonstrate that HCTD consistently outperforms existing methods in identifying both current and emerging influential nodes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".