Unweighted graph
An unweighted graph is a graph whose edges have no assigned numerical weights.
Classic Computer Science Problems in Swift: Essential techniques for practicing programmers
An unweighted graph is a graph whose edges do not have numerical costs or distances.
- How it is used
- Represents bidirectional networks and supports shortest-path questions based on edge count.
- How deep it goes
- Defines edges and adjacency-list storage, implements Swift types, and applies BFS without treating weighted costs.
- Examples
- Uses a Hyperloop-style city network and computes the Boston-to-Miami route through Detroit and Washington.
- What you can do after
- Build it from scratch in Swift and find fewest-edge routes with breadth-first search.
Graph Algorithms for Data Science
An unweighted graph is a graph in which all relationships have the same strength or traversal cost and no relationship weight distinguishes them.
- How it is used
- Uses equal-cost relationships to model and analyze network structure without distinguishing connection strength.
- How deep it goes
- Defines equal relationship cost, contrasts numeric weights, and applies the distinction to a directed follower network.
- Examples
- Uses a Twitter follower network with User nodes and directed FOLLOWS relationships lacking strength attributes.
- What you can do after
- Apply it via Cypher and Neo4j Graph Data Science algorithms to characterize connectedness, density, communities, and centrality.
Read how Graph Algorithms for Data Science explains unweighted graph →
Graph Neural Networks in Action
An unweighted graph is a graph in which all edges have equal importance or weight.
- How it is used
- Represents graphs whose edges all have equal importance, including the sensor network used to set up a spatiotemporal graph problem.
- How deep it goes
- Defines unweighted edges through binary adjacency-matrix entries and contrasts them with weighted edges, without detailing implementation.
- Examples
- Uses a sensor network with 31 positions and a simple binary adjacency matrix as examples.
- What you can do after
- Evaluate whether graph connections are represented as equal-weight binary entries or differentiated edge values.
Read how Graph Neural Networks in Action explains unweighted graph →
Related concepts
- weighted graphcontrasts with · A weighted graph is a graph whose edges have associated values representing quantities such as cost, distance, strength, or frequency.
- graphkind of · A graph is a mathematical structure consisting of vertices connected by edges that represent relationships or connections.
- adjacency matrixuses · An adjacency matrix is a square matrix whose rows and columns represent graph vertices and whose entries indicate edges or encode edge weights between vertex pairs.
- breadth-first searchused by · Breadth-first search is a graph or tree traversal algorithm that explores nodes in nondecreasing order of their distance from a starting node.
- unweighted edgehas part · A graph edge that represents a connection without an associated weight.
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