Edge weight
An edge weight is a quantitative value associated with a graph edge that represents a property of the connection, such as its strength, cost, or distance.
Grokking Algorithms, Second Edition
An edge weight is a numerical value assigned to a graph edge to represent traversal cost, distance, time, or another quantity.
- How it is used
- Represents the cost of each graph connection so routes can be compared by total cost rather than segment count.
- How deep it goes
- Defines weights and walks through route totals, then shows how code stores and adds them.
- Examples
- Uses travel times between nodes and the money required for successive trades toward a piano.
- What you can do after
- Apply it via Dijkstra’s algorithm to compare nonnegative-weight routes and update costs when a cheaper path appears.
Read how Grokking Algorithms, Second Edition explains edge weight →
Advanced Algorithms and Data Structures
A weight is a numerical value associated with a graph edge to represent its cost, distance, or other measure.
- How it is used
- Measures graph-route cost so shortest-path algorithms can optimize distance, time, or another domain-specific objective.
- How deep it goes
- Defines path totals and priority changes, then connects them to priority-queue choices and Dijkstra running times.
- Examples
- Uses intersection maps, competing routes, road distances, and changing travel times caused by traffic or closures.
- What you can do after
- Apply it via Dijkstra’s algorithm to compare routes by accumulated cost and select an appropriate priority-queue implementation.
Read how Advanced Algorithms and Data Structures explains edge weight →
D3.js in Action, Third Edition
An edge weight is a numeric value associated with a network edge that represents the strength or quantity of its connection.
- How it is used
- Uses numeric edge values to prepare network links and communicate relationship strength in visualizations.
- How deep it goes
- Explains storage, bidirectional matrix preparation, opacity mapping, legends, and tooltips without developing broader network analysis.
- Examples
- Demonstrates shared-scene counts among Lady Capulet, the Nurse, Juliet Capulet, and Romeo Montague.
- What you can do after
- Apply it via D3.js to encode connection strength with color, opacity, legends, and interactive tooltips.
Read how D3.js in Action, Third Edition explains edge weight →
Related concepts
- Dijkstra's algorithmused by · Dijkstra's algorithm computes shortest paths from a source vertex in a weighted graph with nonnegative edge weights.
- edge labelcontrasts with · An edge label is a value associated with a graph or tree edge that identifies, describes, or specifies the relationship or key portion represented by that edge.
- learning rateconfigured by · A learning rate is a step-size parameter that controls the magnitude of updates made by an iterative learning or optimization process.
- backpropagationconfigured by · Backpropagation is a procedure for computing derivatives of an output or loss with respect to inputs and parameters by applying the chain rule backward through a computational graph.
- forward passused by · A forward pass is a calculation that propagates inputs through an ordered computational or dependency structure to produce outputs or derived values.
- language modelpart of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
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