Spatial Event Prediction via Multivariate Time Series Analysis of Neighboring Social Units using Deep Neural Networks
Bibliographic record
Abstract
Event prediction in social network structures is a crucial research problem in social network analysis. This impels understanding the intrinsic relationship patterns preserving a given social network structure, based on the study of several structural properties computed on the constituent social units, with respect to space and time. In this regard, tackling problems of this nature is considered NP-Complete. Consequently, this paper proposes an original and unique approach which involves making event predictions about a target social unit, y, based on the intrinsic patterns of relationship learnt from one or more neighboring social units. Our methodology is based on Deep Learning (DL) architectures, and is developed using deep-layer stacks of Multilayer Perceptron (MLP) appended with an adjustment-bias (ab) vector at the output layer in a bid to improve the accuracy and precision of predictions made with respect to the target unit (or node). Also, we trained and tested our technique on a real world social clique comprising 5 connected cities; thereafter, we performed a comparative analysis of our approach against 9 other models drawn from the fields of Deep Learning, Machine Learning, and Statistics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".