Attending Over Triads for Learning Signed Network Embedding
Bibliographic record
Abstract
Network embedding, which aims at learning distributed representations for nodes in networks, is a critical task with wide downstream applications. Most existing studies focus on networks with a single type of edges, whereas in many cases, the edges of networks can be derived from two opposite relationships, yielding signed networks. This paper studies network embedding for the signed network, and a novel approach called \textbf{TEA} is proposed. Similar to existing methods, \textbf{TEA} learns node representations by predicting the sign of each edge in the network. However, many existing methods only consider the local structural information (i.e., the representations of nodes in an edge) for prediction, which can be biased especially for sparse networks. By contrast, \textbf{TEA} seeks to leverage the high-order structures by drawing inspirations from the Social Balance Theory. More specifically, for an edge linking two nodes, \textbf{TEA} predicts the edge sign by treating the paths connecting the two nodes as features. Meanwhile, an attention mechanism is proposed, which assigns different weights to the paths and further weighted combines them for more precise prediction. We conduct experiments on several real-world signed networks, and the results prove the effectiveness of \textbf{TEA} over many strong baseline approaches.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".