Behavior Graph lets you build your programs out of small, easily understood pieces in a way that lets the computer do more of the work for you.
Simple, extensible behavior graph engine
Simple, extensible behavior graph engine
MCP server for authoring and editing Unity Behavior Graph (com.unity.behavior) assets from AI agents
Simple, extensible behavior graph engine
Simple, extensible behavior graph engine
Drag and drop SVG, HTML or Canvas using mouse or touch input.
Simple, extensible behavior graph engine
AWS SDK for JavaScript Detective Client for Node.js, Browser and React Native
Promisified version of cross-spawn
Simple dependency graph.
Use logical overscroll behavior properties and values in CSS
Directed acyclic graph functions for graphology.
Get the graph of dependents in a monorepo
Microsoft Graph Client Library
Example usage: ```javascript import { createClient, Graph } from 'redis';
Pan and zoom SVG, HTML or Canvas using mouse or touch input.
This package contains rich text helpers for Lexical.
2D force-directed graph rendered on HTML5 canvas
Returns `true` if the given string looks like a glob pattern or an extglob pattern. This makes it easy to create code that only uses external modules like node-glob when necessary, resulting in much faster code execution and initialization time, and a bet
Parse sass files and extract a graph of imports
[](https://travis-ci.org/tmont/tarjan-graph) [](https://www.npmjs.com/package/tarjan-graph)
Graph layout for JavaScript
Variant of merge that's useful for webpack configuration
This library allows to map accessors and properties of deeply nested object graph to a plain mapper object with flexible behavior
Using gnuplot and Ruby's benchmarking abilities, see the asymptotic behavior of your functions graphed.
This library allows to map accessors and properties of deeply nested object graph to a plain mapper object with flexible behavior
Flatter transforms a deeply nested graph of ActiveModel-like objects to a single mapper object that handles all the nested attributes and has a very flexible behavior for handling validation, saving routines with a DRY approach.
In computer science, a disjoint-set data structure, also called a union–find data structure or merge–find set, is a data structure that keeps track of a set of elements partitioned into a number of disjoint (non-overlapping) subsets. It provides near-constant-time operations (bounded by the inverse Ackermann function) to add new sets, to merge existing sets, and to determine whether elements are in the same set. In addition to many other uses (see the Applications section), disjoint-sets play a key role in Kruskal's algorithm for finding the minimum spanning tree of a graph. A disjoint-set forest consists of a number of elements each of which stores an id, a parent pointer, and, in efficient algorithms, a value called the "rank". The parent pointers of elements are arranged to form one or more trees, each representing a set. If an element's parent pointer points to no other element, then the element is the root of a tree and is the representative member of its set. A set may consist of only a single element. However, if the element has a parent, the element is part of whatever set is identified by following the chain of parents upwards until a representative element (one without a parent) is reached at the root of the tree. Forests can be represented compactly in memory as arrays in which parents are indicated by their array index. Disjoint-set data structures model the partitioning of a set, for example to keep track of the connected components of an undirected graph. This model can then be used to determine whether two vertices belong to the same component, or whether adding an edge between them would result in a cycle. The Union–Find algorithm is used in high-performance implementations of unification. This data structure is used by the Boost Graph Library to implement its Incremental Connected Components functionality. It is also a key component in implementing Kruskal's algorithm to find the minimum spanning tree of a graph. Note that the implementation as disjoint-set forests doesn't allow the deletion of edges, even without path compression or the rank heuristic. Sharir and Agarwal report connections between the worst-case behavior of disjoint-sets and the length of Davenport–Schinzel sequences, a combinatorial structure from computational geometry.
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