Breadth-first search
The term "cyclic graph" is not well-defined. If you mean a graph that is not acyclic, then the answer is 3. That would be the union of a complete graph on 3 vertices and any number of isolated vertices. If you mean a graph that is (isomorphic to) a cycle, then the answer is n. If you are really asking the maximum number of edges, then that would be the triangle numbers such as n (n-1) /2.
Prove that the maximum vertex connectivity one can achieve with a graph G on n. 01. Define a bipartite graph. Prove that a graph is bipartite if and only if it contains no circuit of odd lengths. Define a cut-vertex. Prove that every connected graph with three or more vertices has at least two vertices that are not cut vertices. Prove that a connected planar graph with n vertices and e edges has e - n + 2 regions. 02. 03. 04. Define Euler graph. Prove that a connected graph G is an Euler graph if and only if all vertices of G are of even degree. Prove that every tree with two or more vertices is 2-chromatic. 05. 06. 07. Draw the two Kuratowski's graphs and state the properties common to these graphs. Define a Tree and prove that there is a unique path between every pair of vertices in a tree. If B is a circuit matrix of a connected graph G with e edge arid n vertices, prove that rank of B=e-n+1. 08. 09.
one vertex: 3 two vertices: 6 three vertices: 8 total 17
n-1
V*(V-1)/2
Dijkstra's algorithm is a more advanced version of breadth-first search in graph traversal. While both algorithms explore nodes in a graph, Dijkstra's algorithm considers the weight of edges to find the shortest path, whereas breadth-first search simply explores nodes in a level-by-level manner.
Use a simple DFS/BFS traversal. If you have gone through all nodes, the graph is connected.
The space complexity of the Dijkstra algorithm is O(V), where V is the number of vertices in the graph.
The average running time of Dijkstra's algorithm for finding the shortest path in a graph is O(V2), where V is the number of vertices in the graph.
n-k-1
The runtime complexity of Kruskal's algorithm is O(E log V), where E is the number of edges and V is the number of vertices in the graph.
The time complexity of the Kosaraju algorithm for finding strongly connected components in a directed graph is O(V E), where V is the number of vertices and E is the number of edges in the graph.
The runtime complexity of Prim's algorithm is O(V2) or O(E log V), where V is the number of vertices and E is the number of edges in the graph.
The space complexity of the breadth-first search algorithm is O(V), where V is the number of vertices in the graph being traversed.
The vertex cover greedy algorithm helps in selecting the minimum number of vertices in a graph to cover all edges. It works by choosing vertices that cover the most uncovered edges at each step, leading to an efficient way to find a minimum vertex cover.
The algorithm used to find all pairs shortest paths in a graph efficiently is called the Floyd-Warshall algorithm. It works by iteratively updating the shortest path distances between all pairs of vertices in the graph until the optimal solution is found.
The key steps in implementing a graph coloring algorithm are: Represent the graph using data structures like adjacency lists or matrices. Choose a coloring strategy, such as greedy coloring or backtracking. Assign colors to vertices based on the chosen strategy, ensuring adjacent vertices have different colors. Repeat the coloring process until all vertices are colored. Validate the coloring to ensure it is valid and optimal.