User Identification in Online Social Networks using Graph Transformer Networks
Why this work is in the frame
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Bibliographic record
Abstract
The problem of user recognition in online social networks is driven by the need for higher security. Previous recognition systems have extensively employed content-based features and temporal patterns to identify and represent distinctive characteristics within user profiles. This work reveals that semantic textual analysis and a graph representation of the user’s social network can be utilized to develop a user identification system. A graph transformer network architecture is proposed for the closed-set node identification task, leveraging the weighted social network graph as input. Users retweeting, mentioning, or replying to a target user’s tweet are considered neighbors in the social network graph and connected to the target user. The proposed user identification system outperforms all state-of-the-art systems. Moreover, we validate its performance on three publicly available 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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