Graph Theory Algorithms in Java

Learn about six graph types—unweighted and weighted, directed and undirected—featuring positive and negative weights, with examples and terminology for graph data structures.

Learn to represent an unweighted undirected graph using an adjacency matrix, with zero/one entries and symmetric edges, then extend to weighted undirected graphs with weights and edge operations.

Learn how to represent directed graphs in Java using adjacency matrices for unweighted and weighted graphs, including how edges and weights are stored, and how to generate text representations.

Explore representing graphs with adjacency lists, including weighted undirected and weighted directed graphs. Implement adjacency lists with nodes containing destination, weight, and next pointer, add edges, and print the graph.

Explore graph representation using adjacency lists for unweighted undirected and directed graphs, building an array of linked lists to store vertices and edges, and printing the adjacency list.

Explore BFS and DFS algorithms for graph traversal, using a queue and visited set for BFS, and a stack for DFS, with adjacency list or matrix representations.

Master BFS on matrices by performing level-by-level traversal using a queue and a visited matrix. Learn how to apply the pattern to graph problems and implement in Java.

Learn to perform bfs traversal on a graph using an adjacency list, starting from a node, marking visited vertices, and using a queue.

Explore depth-first search on matrices with a recursive Java implementation, visiting each cell once using a boolean visited grid and a down-up-right-left traversal order.

Explore depth-first search on an adjacency list in Java, using a visited array and a DFS order list to traverse from a start node and handle disconnected graphs.

Explore the number of islands on a 2d grid of ones and zeros by performing depth-first search to connect lands horizontally or vertically and mark visited cells in place.