A lightweight, efficient, and easy-to-use generic graph data structure and algorithm library for Go.
GoGraph is a generic graph library for the Go programming language that provides implementations of fundamental graph data structures and algorithms. It solves the problem of needing a reliable, type-safe way to work with graphs in Go applications, offering support for directed, undirected, weighted, and acyclic graphs along with traversal and pathfinding algorithms.
Go developers who need to implement graph-based logic in their applications, such as those working on network analysis, dependency resolution, route planning, or social graph features.
Developers choose GoGraph for its clean generic API, comprehensive algorithm coverage, and lightweight design. It provides a production-ready alternative to building graph logic from scratch while being well-documented and easy to integrate into Go projects.
A golang generic graph library that provides mathematical graph-theory and algorithms.
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Uses Go's generics for a clean, type-safe API that supports any comparable vertex label, as demonstrated in the Graph[T comparable] interface examples, preventing runtime errors.
Includes essential algorithms like BFS, DFS, topological sort, Dijkstra, Bellman-Ford, and Floyd-Warshall, covering most common graph operations needed in practical applications.
Supports directed, undirected, acyclic, and weighted graphs out of the box, with clear code snippets in the README for each type, reducing setup complexity.
Designed to be both easy for learning graph theory and efficient for production use, with a lightweight codebase and well-documented examples, as seen in the traversal and connectivity sections.
The library restricts vertex labels to comparable types, explicitly excluding slices and functions in the README, which can be limiting for modeling complex or nested data.
The documentation doesn't mention thread-safety or parallel algorithms, making it less suitable for concurrent applications without manual synchronization, a potential bottleneck for scalable systems.
As a standalone library, it lacks pre-built connectors to graph databases, visualization tools, or other Go data science libraries, requiring extra effort for broader workflow integration.