Correlation constraint shortest path over large multi-relation graphs
Bibliographic record
Abstract
Multi-relation graphs intuitively capture the heterogeneous correlations among real-world entities by allowing multiple types of relationships to be represented as entity-connecting edges, i.e., two entities could be correlated with more than one type of relationship. This is important in various applications such as social network analysis, ecology, and bio-informatics. Existing studies on these graphs usually consider an edge label constraint perspective, where each edge contains only one label and each edge is considered independently. For example, there are lines of research focusing on reachability between two vertices under a set of edge label constraints, or finding paths whose consecutive edge labels satisfy a user-specified logical expression. This is too restricted in real graphs, and in this work, we define a generic correlation constraint on multi-relation graphs from the perspective of vertex correlations, where a correlation can be defined recursively. Specifically, we formalize and investigate the shortest path problem over large multi-relation graphs in the presence of both necessity and denial constraints, which have various real applications. We show that it is nontrivial to apply conventional graph traversal algorithms (e.g., BFS or DFS) to address the challenge. To effectively reduce the search space, we propose a Hybrid Relation Encoding method, a.k.a. HyRE, to encode both topological and relation information in a compact way. We conduct extensive experiments over large real-world graphs to validate the effectiveness and efficiency of the proposed solution.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".