HubScoreResult
HubScoreResult(
scores,
eigenvector,
distance_to_center,
eigenvector_z,
distance_z,
)Result of hub score computation.
Hub nodes are structural centers that hold the embedding space together. They are characterized by: - High eigenvector centrality (in tightly woven neighborhoods) - Low distance to global centroid (central position in space)
Unlike PageRank, which rewards being pointed to by many nodes (both categories AND popular instances qualify), eigenvector centrality rewards being in a tightly interconnected cluster. Abstract concepts cluster more tightly than popular instances.
Attributes
| Name | Type | Description |
|---|---|---|
| scores | np.ndarray | Hub score for each point (higher = more structural/general) |
| eigenvector | np.ndarray | Eigenvector centrality for each point |
| distance_to_center | np.ndarray | Distance to global centroid for each point |
| eigenvector_z | np.ndarray | Z-scored eigenvector centrality |
| distance_z | np.ndarray | Z-scored distance to center |
Methods
| Name | Description |
|---|---|
| get_hub_nodes | Return indices of nodes with hub score above threshold. |
| get_top_hubs | Return indices of top k hub nodes. |
get_hub_nodes
HubScoreResult.get_hub_nodes(threshold=0.0)Return indices of nodes with hub score above threshold.
get_top_hubs
HubScoreResult.get_top_hubs(k=100)Return indices of top k hub nodes.