Topology-based querying treats a graph as a living structure rather than a static index. Instead of asking for “everything about a topic,” you ask for a structural pattern: a hub, a bridge, a sparse frontier, or a repeated motif. You search for shape, not labels.
Why Structure Beats Topics
Topics are human labels. They are useful but limiting. A graph can encode relationships that language cannot easily name. By querying the structure directly, you surface latent concepts without needing to define them first.
Structural Patterns to Query
- Dense hubs: foundational concepts with many connections.
- Bridging nodes: ideas that connect otherwise separate domains.
- Boundary nodes: concepts at the edge of a cluster, often novel or interdisciplinary.
- Motifs: recurring subgraph shapes that indicate specific conceptual roles.
How You Query
You use graph languages to search for shapes: “Find nodes that connect three communities,” or “Find subgraphs with high betweenness and low degree.” These queries are more complex than keyword search, but they unlock deeper retrieval.
Adaptive Retrieval
As the graph evolves, structural queries adapt automatically. A concept can shift from core to boundary as new data arrives. You do not need to re-label it; the topology changes and retrieval follows.
Multi-Perspective Retrieval
You can retrieve the same concept under different lenses by adjusting query criteria. For sustainability, you might prioritize nodes near environmental hubs. For engineering, you might prioritize nodes near structural hubs. This creates a multi-perspective model of meaning.
Structural retrieval turns the graph into a reasoning tool. You are not just finding content; you are navigating emergent meaning.