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.
Welch-Powell's algorithm is a graph coloring algorithm used to color the vertices of a graph such that no two adjacent vertices share the same color. It operates by sorting the vertices in descending order of their degrees and then assigning colors in a greedy manner, ensuring that each vertex receives the lowest available color that hasn't been assigned to its adjacent vertices. This approach is efficient for generating a proper vertex coloring, particularly for sparse graphs. The algorithm is notable for being simple to implement and often yields a near-optimal coloring.
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.