SEERa: A Framework for Community Prediction
Bibliographic record
Abstract
Online user communities exhibit distinct temporal dynamics in response to popular topics or breaking events. Despite abundant community detection libraries, there is yet to be one that provides access to the possible user communities in future time intervals. To bridge this gap, we contribute SEERa, an open-source end-to-end community prediction framework to identify future user communities in a text streaming social network. SEERa incorporates state-of-the-art temporal graph neural networks to model inter-user topical affinities at each time interval via streams of temporal graphs. This all takes place while users' topics of interest and hence their inter-user topical affinities are changing over time. SEERa predicts yet-to-be-seen user communities on the final positions of users' vectors in the latent space. Notably, our framework serves as a one-stop-shop to future user communities for Social Information Retrieval and Social Recommendation systems.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".