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Algorithm Examples

Interactive visualizations of graph algorithm implementations

Graph Traversal

Algorithms for visiting nodes in a graph systematically.

Breadth-First Search (BFS) Level-by-level graph exploration Depth-First Search (DFS) Deep exploration before backtracking

Shortest Path

Find the optimal path between nodes with minimum cost or distance.

Dijkstra's Algorithm Single-source shortest paths Bellman-Ford Algorithm Handles negative weights and cycles Floyd-Warshall Algorithm All-pairs shortest paths

Centrality Measures

Identify the most important nodes in a network based on different criteria.

Degree Centrality Node importance by connection count PageRank Link-based importance ranking Betweenness Centrality Nodes that bridge network parts Closeness Centrality Nodes with shortest paths to all others Eigenvector Centrality Influence based on neighbor importance Katz Centrality Eigenvector with base influence guarantee HITS Algorithm Hub and authority scores for directed graphs

Graph Components

Analyze the connectivity and structure of graphs.

Connected Components Find isolated subgraphs

Minimum Spanning Tree

Find the minimum weight tree that connects all vertices.

Kruskal's Algorithm Build MST by sorting edges Prim's Algorithm Grow MST from a starting vertex

Pathfinding

Algorithms for finding optimal paths in graphs.

A* Algorithm Smart pathfinding with heuristics

Community Detection

Discover groups and clusters within networks.

Louvain Method Fast community detection via modularity Label Propagation Nodes adopt neighbors' most common label Girvan-Newman Algorithm Hierarchical community detection via edge betweenness Leiden Algorithm Improved Louvain with well-connected communities

Clustering

Decompose and analyze graph structure through clustering techniques.

K-Core Decomposition Find densely connected cores Hierarchical Clustering Build tree of clusters by merging closest pairs Spectral Clustering Use eigenvectors to find non-convex clusters MCL (Markov Clustering) Flow-based clustering via random walks

Network Flow

Analyze flow capacity and optimization in networks.

Ford-Fulkerson Maximum flow through a network Min-Cut Algorithms Find minimum cuts using S-T, Stoer-Wagner, and Karger

Link Prediction

Predict future connections based on network structure and similarity measures.

Common Neighbors Predict links based on mutual connections Adamic-Adar Index Weighted similarity emphasizing rare connections

Graph Matching

Find optimal assignments and structural equivalences in graphs.

Bipartite Matching Maximum matching between two disjoint sets Graph Isomorphism Test if two graphs have identical structure