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Graph Neural Networks: Theory & PyTorch
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Module 1
Foundations of Graph Theory
1
Introduction to Graphs: Nodes, Edges, and Direction
Define graphs, nodes, and edges, and distinguish directed vs undirected graphs
2
Graph Representations: Adjacency Matrices & Lists
Represent graphs using adjacency matrices and adjacency lists
3
Graph Terminology Basics
Define node degree, neighbors, and weighted edges
4
Special Graph Types: Bipartite, Complete, and Cyclic
Identify special graph types: bipartite, complete, and cyclic graphs
5
Breadth-First Search (BFS) on Graphs
Perform breadth-first search (BFS) on a graph
6
Depth-First Search on Graphs
Perform depth-first search (DFS) on a graph
7
Graph Connectivity: BFS/DFS Applications
Identify connected components in a graph using BFS or DFS
8
BFS for Shortest Path in Unweighted Graphs
Find the shortest path between two nodes in an unweighted graph using BFS
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