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M1_VOL3_GRAPHTHEORY 📈
explain all concepts from this pdf with detailed structure , examples, related questions & answers with ease to understandHere is a structured, detailed explanation of all major concepts in the PDF M1_VOL3_GRAPHTHEORY.pdf, including definitions, examples, related questions, and answers—all presented for easy understanding1.
1. Graphs and General Graph Problems 1.1 Introduction Concept: Graphs model pairwise relationships between objects. Example: Social networks (people as vertices, friendships as edges), communication networks (devices as vertices, links as edges)1. Key Idea: Graphs abstract real-world situations by focusing on connections rather than physical layout. 1.2 Graph Definition: A graph $ G = (V, E) $ consists of a set of vertices (nodes) $ V $ and a set of edges $ E $ connecting pairs of vertices. Example: $ V = {A, B, C, D, E, F, G} $, $ E = {(A,B), (A,C), (B,D), (B,E), (C,F), (C,G)} $1. Undirected Graph: Edges have no direction; if $(A, B)$ is present, so is $(B, A)$ implicitly. 1.3 Types of Graphs Simple Graph: No loops or multiple edges between the same pair of vertices. Directed Graph: Edges have direction; $(A, B)$ does not imply $(B, A)$. Undirected Graph: All edges are bidirectional. Complete Graph: Every pair of distinct vertices is connected by an edge. Example: A complete graph with 4 vertices has every vertex connected to every other vertex. 1.4 Paths and Reachability Path: A sequence of vertices connected by edges. Example: $ A \rightarrow B \rightarrow C \rightarrow D $ is a path from $ A $ to $ D $1. Reachability: Vertex $ u $ is reachable from $ v $ if there is a path from $ v $ to $ u $. Example: In a social network, if Alice is connected to Bob, who is connected to Charlie, Alice is reachable from Charlie via Bob. 1.5 More on Graphs 1.5.1 Graph Coloring Definition: Assign colors to vertices so that no two adjacent vertices have the same color. Chromatic Number: Minimum number of colors needed. Example: Scheduling classes so that conflicting classes (edges) are not at the same time (color). Result: 2 colors may suffice for some graphs1. Related Question: What is the minimum number of colors required for a given graph? Answer: Depends on the graph; for the example in the PDF, it is 2. 1.5.2 Vertex Cover Definition: A set of vertices such that every edge is incident to at least one vertex in the set. Example: In a graph, ${2,4,5}$ may be a vertex cover1. Related Question: Find a vertex cover for a given graph. Answer: For the example, ${2,4,5}$ is a vertex cover. 1.5.3 Independent Set Definition: A set of vertices where no two are adjacent. Example: ${1,4,6}$ may be a maximum independent set1. Related Question: Find a maximum independent set. Answer: For the example, ${1,4,6}$ is a maximum independent set. 1.5.4 Matching Definition: A set of edges without common vertices. Example: ${(1,2), (3,4), (5,6)}$ is a matching1. Related Question: Find a maximum matching. Answer: For the example, ${(1,2), (3,4), (5,6)}$ is a maximum matching. 1.6 Representing Graphs 1.6.1 Adjacency Matrix Definition: A square matrix where $ A_{ij} = 1 $ if there is an edge between vertices $ i $ and $ j $, else 0. Example: $$ A = \begin{pmatrix} 0 & 1 & 1 & 0 \\ 1 & 0 & 0 & 1 \\ 1 & 0 & 0 & 1 \\ 0 & 1 & 1 & 0 \end{pmatrix} $$ Related Question: Find the adjacency matrix for a given graph. Answer: See above matrix for the example. 1.6.2 Adjacency List Definition: For each vertex, list its neighbors. Example: $ A: {B} $ $ B: {A, C, D, E} $ $ C: {B, D} $ $ D: {B, C, E} $ $ E: {B, D} $ 1.7 Breadth-First Search (BFS) Algorithm: Explore all neighbors of a vertex before moving to the next level. Example: Starting from vertex 1, BFS visits: 1, 2, 4, 3, 5, 6, 71. Applications: Shortest path in unweighted graphs. Related Question: Draw the BFS tree starting from vertex $ E $. Answer: The tree will show $ E $ connected to its neighbors, then their neighbors, etc. 1.8 Depth-First Search (DFS) Algorithm: Explore as far as possible along each branch before backtracking. Example: Starting from vertex 4, DFS may visit: 4, 0, 1, 2, 31. Applications: Topological sorting, strongly connected components, maze solving. Related Question: Draw the DFS tree starting from vertex $ E $. Answer: The tree will show a path as deep as possible before backtracking. 1.9 Degree of a Vertex Definition: Number of edges incident to a vertex (undirected graph). Example: In a complete graph with 4 vertices, each vertex has degree 3. Related Question: What is the degree of each vertex in a given graph? Answer: For the complete graph, all degrees are 3. 1.10 Indegrees and Outdegrees Indegree: Number of edges entering a vertex (directed graph). Outdegree: Number of edges leaving a vertex (directed graph). Example: For a directed graph, sum of indegrees equals sum of outdegrees. Related Question: What are the indegree and outdegree of each vertex? Answer: For the example, indegree sequence is (1,1,1,0), outdegree is (1,2,1,0). 1.11 Problems Example Question: Find the shortest path connecting two people in a social network. Answer: Use BFS to find the shortest path. More Questions: Find adjacency matrix, vertex cover, independent set, BFS/DFS trees, chromatic number, etc. 2. DAGs, Topological Sorting, and Longest Path 2.1 Directed Acyclic Graph (DAG) Definition: Directed graph with no directed cycles. Example: Task dependencies in project scheduling. Related Question: Why is a given graph not a DAG? Answer: Because it contains a directed cycle. 2.2 Topological Sorting Definition: Linear ordering of vertices such that for every directed edge $(u, v)$, $u$ comes before $v$. Algorithm: Repeatedly pick vertices with indegree 0, remove them, and update indegrees. Example: For a DAG, one possible topological order is $A, B, D, E, C, F$1. Related Question: Find a topological sorting for a given DAG. Answer: $A, B, D, E, C, F$ is one possible order. 2.3 Longest Path in a DAG Algorithm: Topologically sort the graph, then for each vertex, update the longest path to its neighbors. Example: In a DAG, the longest path can be found using dynamic programming after topological sort1. 2.4 Transitive Closure Definition: A graph that includes an edge $(u, v)$ if there is a path from $u$ to $v$ in the original graph. Example: If there are paths $A \rightarrow B \rightarrow C$, then the transitive closure includes $A \rightarrow C$1. Related Question: Find the transitive closure of a given graph. Answer: Add edges for all reachable pairs. 2.5 Matrix Multiplication Adjacency Matrix: Represents graph connectivity. Reachability Matrix: $A^k$ gives paths of length $k$. Transitive Closure Matrix: $A + A^2 + A^3 + \ldots + A^n$. Example: For a graph, compute $A^2$ to find paths of length 21. Related Question: Compute $A^2$ for a given adjacency matrix. Answer: Multiply the matrix by itself. 2.6 Problems Example Question: Which relation represents the transitive closure? Answer: The relation that includes all reachable pairs. More Questions: Find matrix powers, topological sorting, longest path, etc. 3. Weighted Graphs and Shortest Path Algorithms 3.1 Weighted Graph Definition: Each edge has a weight (distance, cost, time). Example: Cities connected by roads with distances1. 3.2 Dijkstra’s Algorithm Algorithm: Finds shortest path from a source to all other vertices in a graph with non-negative weights. Example: Shortest path from $A$ to $D$: $A \rightarrow C \rightarrow D$ with total weight 31. Related Question: Find the shortest path from $A$ to $D$. Answer: $A \rightarrow C \rightarrow D$ with weight 3. 3.3 Bellman-Ford Algorithm Algorithm: Finds shortest paths from a source in graphs with negative weights (no negative cycles). Example: After iterations, shortest distances from $A$: $A(0), B(-1), C(2), D(1), E(4)$1. Related Question: What are the shortest distances after Bellman-Ford? Answer: As above. 3.4 Spanning Trees Definition: A subgraph that is a tree and connects all vertices. Example: A tree connecting all cities with minimum total road length1. 3.5 Prim’s Algorithm Algorithm: Greedily adds the shortest edge connecting a tree vertex to a non-tree vertex. Example: Starting from $A$, add edges $(A,C)$, $(C,E)$, etc., to form a minimum spanning tree1. Related Question: Find the minimum spanning tree using Prim’s algorithm. Answer: Add edges in order of smallest weight, avoiding cycles. 3.6 Kruskal’s Algorithm Algorithm: Adds edges in order of increasing weight, skipping those that form cycles. Example: Add edges $(B,D)$, $(A,C)$, $(A,F)$, etc., to form a minimum spanning tree1. Related Question: Find the minimum spanning tree using Kruskal’s algorithm. Answer: Add edges in order of increasing weight, skipping those that form cycles. 3.7 Problems Example Question: At what time will city $G$ start flooding if water spreads along weighted edges? Answer: 8 minutes1. More Questions: Find shortest paths, minimum spanning trees, order of edge addition, etc. 4. Answers to Selected Questions Graph Coloring: Minimum number of colors is 2 for the example graph. Vertex Cover: ${2,4,5}$ is a vertex cover. Independent Set: ${1,4,6}$ is a maximum independent set. Matching: ${(1,2), (3,4), (5,6)}$ is a maximum matching. BFS/DFS Trees: See above for examples. Degree/Indegree/Outdegree: See above for examples. Topological Sorting: $A, B, D, E, C, F$ is one possible order. Transitive Closure: Add edges for all reachable pairs. Dijkstra’s Algorithm: Shortest path from $A$ to $D$ is $A \rightarrow C \rightarrow D$ with weight 3. Bellman-Ford: Shortest distances from $A$ are $A(0), B(-1), C(2), D(1), E(4)$. Prim’s/Kruskal’s: Add edges in order of smallest weight, avoiding cycles. Summary Table Concept Definition/Algorithm Example/Question Answer/Explanation Graph $G = (V, E)$ Social network Vertices: people, edges: friendships Path/Reachability Sequence of connected vertices $A \rightarrow B \rightarrow C$ Path from $A$ to $C$ Graph Coloring Color vertices, no two adjacent same Scheduling classes 2 colors for example graph Vertex Cover Set covers all edges ${2,4,5}$ Covers all edges Independent Set No two vertices adjacent ${1,4,6}$ Maximum independent set Matching No two edges share a vertex ${(1,2), (3,4), (5,6)}$ Maximum matching Adjacency Matrix $A_{ij} = 1$ if edge $i-j$ See matrix above Represents graph connectivity BFS Explore level by level 1, 2, 4, 3, 5, 6, 7 Shortest path in unweighted DFS Explore as deep as possible 4, 0, 1, 2, 3 Topological sorting, etc. Degree Number of edges at vertex 3 in complete 4-vertex graph All degrees 3 Indegree/Outdegree Edges in/out (directed graph) Indegree: (1,1,1,0), Outdegree: (1,2,1,0) Sums equal DAG No directed cycles Task dependencies No cycles Topological Sorting Linear order, edges $u \rightarrow v$, $u$ before $v$ $A, B, D, E, C, F$ One possible order Longest Path (DAG) Dynamic programming after topo sort See example Longest path found Transitive Closure Add edges for all reachable pairs $A \rightarrow C$ if $A \rightarrow B \rightarrow C$ All reachable pairs Matrix Multiplication $A^k$ gives paths of length $k$ See matrix multiplication Paths of length $k$ Dijkstra’s Algorithm Shortest path, non-negative weights $A \rightarrow C \rightarrow D$ Shortest path, weight 3 Bellman-Ford Shortest path, negative weights $A(0), B(-1), C(2), D(1), E(4)$ Shortest distances Spanning Tree Tree connecting all vertices See example Connects all, no cycles Prim’s Algorithm Greedy, add smallest edge $(A,C), (C,E), \ldots$ Minimum spanning tree Kruskal’s Algorithm Add edges in order, avoid cycles $(B,D), (A,C), \ldots$ Minimum spanning tree This structured approach covers all major concepts in the graph theory PDF, with clear definitions, examples, and answers to related questions1.