Machine Learning and Graph-Based Models for Citation Network Analysis
Bibliographic record
Abstract
Forecasting how research papers will be cited in the future is a meaningful yet complex problem, particularly relevant to academic assessment, research funding, and institutional planning. In this work, we explore multiple modeling techniques—ranging from traditional machine learning to deep learning and graph-based approaches—to estimate citation counts using data from the DBLP v14 collection. A citation network was built to capture the relationships among papers, and features were extracted based on both direct citations and indirect, multi-layered citation links. We evaluated several predictive models, including Logistic Regression, Random Forest, RNN, LSTM, and GRU. Our findings suggest that direct citation data leads to more stable and accurate predictions, while incorporating indirect citation layers does not yield significant benefits. Among the models tested, the GRU network delivered the most accurate results, achieving a Root Mean Squared Error (RMSE) of 3.78 for five-year citation forecasts. These results underscore the value of temporal modeling and reinforce the effectiveness of sequence-based architectures in understanding citation dynamics.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".