DyHNet: Learning Dynamic Heterogeneous Network Representations
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Many real-world networks, such as social networks, contain structuralheterogeneity and experience temporal evolution. However, while therehas been growing literature on network representation learning, only afew have addressed the need to learn representations for dynamic hetero-geneous networks. The objective of our work in this paper is to introduce DyHNet, which learns representations for such networks and distinguishesitself from the state-of-the-art by systematically capturing (1) local nodesemantics, (2) global network semantics, and (3) longer-range temporalassociations between network snapshots when learning network repre-sentations. Through experiments on four real-world datasets, we demon-strate that our proposed method is able to show consistently better andmore robust performance compared to the state-of-the-art techniques.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.000 | 0.007 |
| 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