Clarifying Report: Graph Traversal, Visit Frequencies, and Priority Queue
1. BFS Traversal Snapshot
This section shows how each node was discovered during the breadth-first search (BFS), along with its parent in the traversal tree. Repeated entries reveal multiple incoming edges or cycles.
| Current Node | Parent Node | Occurrences |
|---|---|---|
| org.nanotek.beans.entity.Recording | org.nanotek.beans.entity.Isrc | 1 |
| org.nanotek.beans.entity.ArtistCredit | org.nanotek.beans.entity.Recording | 9 |
| org.nanotek.beans.entity.Release | org.nanotek.beans.entity.Medium | 3 |
| org.nanotek.beans.entity.ReleasePackaging | org.nanotek.beans.entity.Release | 4 |
| org.nanotek.beans.entity.Language | org.nanotek.beans.entity.Release | 4 |
Total unique edges traversed: 28.
2. Visit Frequency Summary
After completing BFS, this table shows how often each class was enqueued (visited). High values flag hubs; low values flag leaves or isolated nodes.
| Class | Visit Count |
|---|---|
| ArtistCredit | 9 |
| AreaType | 5 |
| ReleaseGroupPrimaryType | 5 |
| ReleasePackaging | 4 |
| Language | 4 |
| ReleaseGroup | 4 |
| ReleaseStatus | 4 |
| Area | 4 |
| Recording | 3 |
| Release | 3 |
| LabelType | 2 |
| ArtistType | 2 |
| Gender | 2 |
| MediumFormat | 1 |
| RecordingAliasType | 1 |
| InstrumentType | 1 |
| ReleaseAliasType | 1 |
| Artist | 1 |
| WorkType | 1 |
| Label | 1 |
| ArtistAliasType | 1 |
Total visited elements: 21 of 31 possible nodes.
3. Priority Queue After Scoring
This table lists each class with its computed priority score, sorted from highest to lowest.
| Class | Priority Score |
|---|---|
| ArtistCredit | 31 |
| AreaType | 24 |
| Recording | 19 |
| Area | 15 |
| Release | 15 |
| ReleaseAliasType | 14 |
| Track | 11 |
| ArtistType | 9 |
| ReleaseLabel | 9 |
| Artist | 8 |
| MediumFormat | 7 |
| ReleaseGroup | 7 |
| RecordingAliasType | 7 |
| Label | 5 |
| ArtistAliasType | 5 |
| InstrumentType | 2 |
| Isrc | 2 |
| WorkType | 2 |
| Work | 1 |
| ArtistAlias | 1 |
| ReleaseAlias | 1 |
| RecordingAlias | 1 |
| Instrument | 1 |
| Medium | 1 |
| ReleaseStatus | 0 |
| ReleaseGroupPrimaryType | 0 |
| Language | 0 |
| LabelType | 0 |
| ReleasePackaging | 0 |
| Gender | 0 |
| BaseType | 0 |
4. Recommendations for Scoring Adjustments
- Apply inverse-frequency scaling:
new_score = raw_score / log(visit_count + e)
This dampens the impact of extremely frequent nodes. - Incorporate depth penalty:
depth_weight = 1 / (1 + distance_from_root)
Multiply by your existing score so deeper nodes aren’t unfairly deprioritized. - Add a “novelty” boost for unvisited or rarely visited nodes
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