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Adjacency Matrix →
Requires prior knowledge of vertices/edges, directed vs. undirected and weighted graphs, and graph density to map nodes to indices, interpret A[i][j] values and symmetry, and reason about the O(1) checks vs O(n^2) space trade-offs.
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Graph →
Understanding the graph data structure depends on the intro’s definitions (nodes, edges), graph types (directed/undirected, weighted/unweighted, dense/sparse), standard representations (adjacency lists/matrices), and baseline traversal/shortest‑path techniques (BFS, DFS) needed to specify, implement, and use graphs correctly.
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Adjacency List →
Understanding graph basics (nodes/edges, direction, weights, sparsity, and neighbors) is required to define an adjacency-list structure, interpret its entries, and evaluate its trade-offs for traversal and path algorithms.
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Breadth First Search →
BFS assumes understanding of nodes/edges, directed vs. undirected graphs, and common graph representations (adjacency lists/matrices) to correctly traverse levels and manage visited state.
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Introduction To Weighted Graphs →
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Directed Acyclic Graph →
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Minimum Spanning Tree →