Welcome to Programming and Data Structures

A graph representation where for each vertex we maintain a list of adjacent vertices.

A graph representation where for each vertex we maintain a map of adjacent vertices.

A two-dimensional table indicating whether an edge exists between pairs of vertices.

A traversal that explores all neighbors at one depth before moving to the next depth.

A traversal that follows one path as far as possible before backtracking.

An algorithm for finding shortest paths from a source vertex in graphs with nonnegative edge weights.

A graph whose edges have direction, going from one vertex to another.

The connections between vertices in a graph.

A data structure made of vertices and edges that represent relationships.

Graphs where neighbors are generated by rules instead of being stored explicitly.

A search strategy that repeatedly runs depth-limited DFS with increasing depth limits.

A conceptual model of a graph focused on vertices and edges rather than storage details.

An edge that starts and ends at the same vertex.

A spanning tree of a weighted graph with the smallest possible total edge weight.

Another name for vertices, the points in a graph.

Multiple edges that connect the same pair of vertices.

A sequence of vertices where consecutive vertices are connected by edges.

A greedy algorithm that builds a minimum spanning tree by repeatedly adding the cheapest connecting edge.

A tree formed by recording how a graph search discovers vertices.

A path that does not repeat vertices.

A linear ordering of vertices in a directed acyclic graph so each edge points forward in the order.

A graph whose edges have no direction and can be traversed either way.

A graph where edges are treated equally and do not carry numeric weights.

The points or nodes in a graph.

A graph whose edges have associated numeric weights or costs.