Graph data structure
In computer science, a graph is an abstract data type that is meant to implement the undirected graph and directed graph concepts from the field of graph theory within mathematics.
At a glance
Key signals
83% cross fields · reaches 4 more
- Computer Science
- Data Structures
- Explanation
- Examples
- Misconception
- Sourced relations
- Attribution
Dependencies
What this concept builds on and what it makes possible — derived from the atlas’s dependency, causal and structural relations, not from every related edge.
System context
Graph data structureis aData structureEstablished
graph data structure is a kind of data structure.
Mechanism: A graph is a data structure of nodes joined by edges, able to model any network of relationships, from maps to social ties.
- Wikidata verifiedmoderate evidence
Edgeis part ofGraph data structureEstablished
Edges join the vertices of a graph.
Mechanism: Each edge records a relationship between two vertices, optionally with a direction and a weight.
Vertexis part ofGraph data structureEstablished
Vertices are the nodes of a graph.
Mechanism: A graph is a set of vertices together with the edges that connect them.
Structural role & consequence
Interpreted from the current atlas graph — what the connections mean, not just how many there are.
Removing this node severs the only sampled structural route between Critical path and Connectivity — a non-redundant bridge here.
structural · Structural removal simulation — not a historical or causal counterfactual.
83% of its relationships cross field boundaries, reaching 4 other disciplines — unusual in a discipline where most concepts stay within their field.
structural · Structural graph analysis — not a claim of importance, causation or history.
Currently dark in the atlas: no key date stored · 6 of 6 of its relations lack claim-level evidence.
atlas representation · Describes the current Thinking OS representation, not the state of the world.
Structural neighbourhood: 6 → 13 → 17 concepts reachable within 3 hops.
structural · Structural reach — being reachable is not the same as being understood.
cross-field
1 within-field, 5 cross-field
Strengths & constraints
Strengths
- Cross-disciplinary connector — 83% of its relationships cross field boundaries. structural
Constraints
- Evidence coverage currently thin in the atlas — few of its relationships carry claim-level evidence. atlas representation
- Non-redundant bridge — removing it severs a sampled route between neighbouring clusters. structural
- No dated history stored — the atlas records no key date for this concept. atlas representation
Conditions
- Read structurally — most of its relationships carry no external evidence yet, so claims here are graph-derived. structural
Seen through each discipline
How this concept sits in each of its fields — derived from its real connections in the graph, not asserted.
Through this lens it connects to Data structure.
Through this lens it connects to Data structure.
Related ideas to explore
Concepts that look related but are not yet connected here — candidates for a connection to reason about, not established links.
This idea also appears in…
The same structure shows up in other disciplines. These are real recurrences drawn from the graph — a starting point for asking “what carries over, and what changes?”
Networks50 disciplines · 35 concepts
Concepts
shares a mental model
shares a mental model
shares a mental model
shares a mental model
shares a mental model
shares a mental model
Mental models at work here
- Connects 6 other ideas across 2 disciplines.
- A cross-disciplinary bridge — its connections reach into 4 other fields.
- Most of its connections are of the “Kind & structure” kind.
- It exercises 1 reusable thinking pattern.
Derived from the graph’s real structure — observations, not a score.
Sources
- Wikipedia (English & German editions) verifiedmoderate evidence
- Wikidata verifiedmoderate evidence