BeComE: A Framework for Node Classification in Social Graphs
Why this work is in the frame
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Bibliographic record
Abstract
In this study, we explore the important role of graph embedding methods in extracting valuable insights from graph structures, specifically focusing on node classification tasks. It is important to know the structural and semantic features and connections within nodes in a graph to learn more detailed hidden patterns. Hence, we propose a hybrid architecture, BeComE (Bert-ComplEx Embedding Model)1, a novel framework that employs both semantic and structural features from social network structures extracted through label-aware embedding models to aid in node classification in social graphs. A Support Vector Machine (SVM) classifier receives these vector embeddings as input features for classification tasks on social graphs and networks. The evaluation shows that BeComE gives better accuracy and F1-score results on the ‘Cora’ and ‘Citeseer’ datasets.
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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 | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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 it