Find answers to common questions about graph theory, algorithms, and our interactive learning platform.
Graph theory is a branch of mathematics that studies graphs - mathematical structures used to model pairwise relations between objects. A graph consists of vertices (nodes) connected by edges (links). It's fundamental to computer science, used in algorithms, data structures, network analysis, and many real-world applications like social networks, transportation systems, and computer networks.
This platform is designed for computer science students, software engineers, researchers, educators, and anyone interested in learning graph algorithms. Whether you're a beginner learning your first graph traversal algorithm or an advanced user exploring complex network flow algorithms, our interactive visualizations make concepts accessible at every level.
Yes! Our interactive graph theory learning platform is completely free to use. You can access all 23+ algorithm visualizations, create custom graphs, and explore step-by-step algorithm execution without any cost. We believe quality education should be accessible to everyone.
We include 23+ essential graph algorithms across multiple categories:
BFS (Breadth-First Search) explores nodes level by level, visiting all neighbors before going deeper. It uses a queue and finds shortest paths in unweighted graphs. DFS (Depth-First Search) explores as deep as possible before backtracking, using a stack (or recursion). BFS is better for shortest paths, while DFS is better for topological sorting and cycle detection.
Use Dijkstra's algorithm when you have non-negative edge weights and need the fastest solution (O((V+E)logV)). Use Bellman-Ford when you have negative edge weights or need to detect negative cycles (O(VE)). Dijkstra is faster but can't handle negative weights, while Bellman-Ford is more versatile but slower.
You can create custom graphs by clicking on the canvas to add nodes, then clicking between nodes to create edges. Use the settings panel to configure graph properties like directed/undirected, weighted edges, and node labels. You can also import graphs from JSON format or generate sample graphs for testing algorithms.
Yes! You can export your graphs in multiple formats including JSON (for data interchange), adjacency matrix (for mathematical analysis), adjacency list (for programming), and JPG image (for presentations and documentation). These exports can be used in other applications or imported back into our platform later.
Our platform works on all modern browsers including Chrome, Firefox, Safari, and Edge. We recommend using the latest version of your browser for the best experience. The platform is also mobile-responsive and works on tablets and smartphones.
While basic programming knowledge is helpful, our visual approach makes graph algorithms accessible to beginners. We provide step-by-step explanations, complexity analysis, and real-world use cases. Start with simple algorithms like BFS and DFS, then progress to more advanced topics as you build confidence.
Start with basic graph concepts and traversal algorithms (BFS, DFS). Then move to shortest path algorithms (Dijkstra, Bellman-Ford), followed by minimum spanning trees (Prim, Kruskal). Practice with different graph types and sizes. Use our interactive visualizations to understand each step, and try implementing the algorithms yourself after understanding the concepts.
Yes! You can create your own practice problems by generating different graph structures and testing various algorithms. Try creating graphs with different properties (dense vs sparse, directed vs undirected, weighted vs unweighted) and observe how algorithms behave. We also provide sample graphs for each algorithm type.
Yes! Our platform is fully responsive and works on tablets and smartphones. While the desktop experience offers the most comprehensive features, you can view algorithm documentation, browse FAQ pages, and access educational content on mobile devices. For the best interactive graph editing and algorithm visualization experience, we recommend using a desktop or laptop computer.
Currently, the platform operates as a session-based tool where your graphs and progress are maintained during your current session. You can export your graphs in multiple formats (JSON, adjacency matrix, adjacency list, and JPG image) to save your work locally. We're working on user accounts and cloud saving features for future releases.
Start exploring our interactive graph algorithms and discover the answers through hands-on learning.