This document discusses depth-first search (DFS) algorithms and their applications to finding spanning trees and articulation points in graphs. It begins by explaining how DFS generalizes preorder tree traversal and avoids cycles in arbitrary graphs by marking visited vertices. DFS takes O(V+E) time on graphs. The document then explains how DFS can be used to find a depth-first spanning tree and identify biconnected components and articulation points via numbering schemes like num(v) and low(v).
It is a algorithm used to find a minimum cost spanning tree for connected weighted undirected graph.This algorithm first appeared in Proceedings of the American Mathematical Society in 1956, and was written by Joseph Kruskal.
The purpose of making this presentation is to explain Kruskal's Algorithm in a simple and attractive way. However, the content of this presentation is taken from more than one resources and merged at one place for the better understanding of students. It also contains an example which will definitely help students to understand it more quickly.
A presentation on prim's and kruskal's algorithmGaurav Kolekar
This slides are for a presentation on Prim's and Kruskal's algorithm. Where I have tried to explain how both the algorithms work, their similarities and their differences.
It is a algorithm used to find a minimum cost spanning tree for connected weighted undirected graph.This algorithm first appeared in Proceedings of the American Mathematical Society in 1956, and was written by Joseph Kruskal.
The purpose of making this presentation is to explain Kruskal's Algorithm in a simple and attractive way. However, the content of this presentation is taken from more than one resources and merged at one place for the better understanding of students. It also contains an example which will definitely help students to understand it more quickly.
A presentation on prim's and kruskal's algorithmGaurav Kolekar
This slides are for a presentation on Prim's and Kruskal's algorithm. Where I have tried to explain how both the algorithms work, their similarities and their differences.
The solution to the single-source shortest-path tree problem in graph theory. This slide was prepared for Design and Analysis of Algorithm Lab for B.Tech CSE 2nd Year 4th Semester.
One of the main reasons for the popularity of Dijkstra's Algorithm is that it is one of the most important and useful algorithms available for generating (exact) optimal solutions to a large class of shortest path problems. The point being that this class of problems is extremely important theoretically, practically, as well as educationally.
This presentation gives a conceptual idea of a graphical algorithm that is Dijkstra's Algorithm. Includes general introduction , the pseudocode, code in form of QR Card, graphical. Also discussing the algorithm in form of graphical images and nodes. Also it include the complexity and application of algorithm in various ranges of fields. This is a fun, eye-catching, conceptual presentation, best suited for students into engineering.
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph.
The solution to the single-source shortest-path tree problem in graph theory. This slide was prepared for Design and Analysis of Algorithm Lab for B.Tech CSE 2nd Year 4th Semester.
One of the main reasons for the popularity of Dijkstra's Algorithm is that it is one of the most important and useful algorithms available for generating (exact) optimal solutions to a large class of shortest path problems. The point being that this class of problems is extremely important theoretically, practically, as well as educationally.
This presentation gives a conceptual idea of a graphical algorithm that is Dijkstra's Algorithm. Includes general introduction , the pseudocode, code in form of QR Card, graphical. Also discussing the algorithm in form of graphical images and nodes. Also it include the complexity and application of algorithm in various ranges of fields. This is a fun, eye-catching, conceptual presentation, best suited for students into engineering.
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph.
In computer science, a data structure is a particular way of organizing data in a computer so that it can be used efficiently. Different kinds of data structures are suited to different kinds of applications, and some are highly specialized to specific tasks.
breadth-first search (BFS) is a strategy for searching in a graph when search is limited to essentially two operations
Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures.
A Graph is a non-linear data structure, which consists of vertices(or nodes) connected by edges(or arcs) where edges may be directed or undirected.
Graphs are a powerful and versatile data structure that easily allow you to represent real life relationships between different types of data (nodes).
Acetabularia Information For Class 9 .docxvaibhavrinwa19
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http://sandymillin.wordpress.com/iateflwebinar2024
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Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
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This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
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2.5 graph dfs
1. WEEK 13
Graphs V
Depth-First Search, Depth-First
Spanning Tree, Biconnectivity
CE222 – Data Structures & Algorithms II
Chapter 9.6.1, 9.6.2
(based on the book by M. A. Weiss, Data Structures and Algorithm Analysis in C++, 3rd edition, 2006)
2012-2013 Spring
2. Depth First Search
• Depth-first search is a generalization of preorder traversal.
• Starting at some vertex, v, we process v and then recursively
traverse all vertices adjacent to v.
• If this process is performed on a tree, then all tree vertices
are systematically visited in a total of O(|E|) time
• Preorder traversal :
– Visit Root
– Visit Left Subtree
– Visit Right Subtree
2
CE 222-Data Structures & Algorithms II,
Izmir University of Economics
3. Depth First Search
• If we perform this process on an arbitrary
graph, we need to be careful to avoid cycles.
• To do this, when we visit a vertex v, we mark
it visited, since now we have been there, and
recursively call depth-first search on all
adjacent vertices that are not already marked.
3
CE 222-Data Structures & Algorithms II,
Izmir University of Economics
4. Depth First Search Algorithm
void dfs( vertex v )
{
visited[v] = TRUE;
for each w adjacent to v
if( !visited[w] )
dfs( w );
}
4
Because this
strategy guarantees
that each edge is
encountered only
once, the total time
to perform the
traversal is O(|E| + |
V|), as long as
adjacency lists are
used.
CE 222-Data Structures & Algorithms II,
Izmir University of Economics
5. Depth First Spanning Tree
The root of the tree is A, the first vertex visited. Each edge
(v, w) in the graph is present in the tree:
•If, when we process (v, w), we find that w is unmarked, or
if, when we process (w, v), we find that v is unmarked, we
indicate this with a tree edge.
•If, when we process (v, w), we find that w is already
marked, and when processing (w, v), we find that v is
already marked, we draw a dashed line, which we will call a
back edge, to indicate that this "edge" is not really part of
the tree.
5
CE 222-Data Structures & Algorithms II,
Izmir University of Economics
6. Undirected graph and Depth First Spanning Tree
6
A preorder numbering of the tree (using only tree edges) will tell
us the order in which vertices are marked. Preorder traversal, in
this case, is:
Visit Root, Visit Children.
If the tree is not connected then processing all nodes requires
several calls to dfs depth first spanning forest
CE 222-Data Structures & Algorithms II,
Izmir University of Economics
7. • A connected undirected graph is biconnected if
there are no vertices whose removal disconnects the
rest of the graph.
Example :
If the nodes are computers and the edges are links,
then if any computer goes down, network mail is
unaffected, except, of course, at the down
computer.
Biconnectivity
7
CE 222-Data Structures & Algorithms II,
Izmir University of Economics
8. Biconnectivity
• If a graph is not biconnected, the vertices
whose removal would disconnect the graph
are known as articulation points. These nodes
are critical in many applications.
8
CE 222-Data Structures & Algorithms II,
Izmir University of Economics
9. Biconnectivity
• The graph below is not biconnected: C and D
are articulation points. The removal of C
would disconnect G, and the removal of D
would disconnect E and F, from the rest of the
graph
9
CE 222-Data Structures & Algorithms II,
Izmir University of Economics
10. Biconnectivity
• Depth-first search provides a linear-time
algorithm to find all articulation points in a
connected graph.
1. Starting at any vertex, perform a depth-first search
and number the nodes as they are visited: num(v).
2. For every vertex v in the depth-first spanning tree,
compute the lowest-numbered vertex called low(v),
that is reachable from v by taking zero or more tree
edges and then possibly one back edge (in that
order).
10
CE 222-Data Structures & Algorithms II,
Izmir University of Economics
11. Biconnectivity
• The depth-first spanning tree shows the
preorder number first, and then the
lowest-numbered vertex reachable
• (For each node num/low is given in the tree)
11
CE 222-Data Structures & Algorithms II,
Izmir University of Economics
12. Biconnectivity
• The lowest-numbered vertex , low(v), can be efficiently
computed by performing a postorder traversal of the
depth-first spanning tree, and it is the minimum of
1) num(v)
2) the lowest num(w) among all back edges (v, w)
3) the lowest low(w) among all tree edges (v, w)
12
CE 222-Data Structures & Algorithms II,
Izmir University of Economics
13. Biconnectivity: How to decide
Articulation Points
• The root is an articulation point if and only if it has more
than one child (because if it has two children, removing
the root disconnects nodes in different subtrees, and if it
has only one child, removing the root merely disconnects
the root.)
• Any other vertex v is an articulation point if and only if v
has some child w such that low(w) ≥ num(v).
Example:
Vertex D is an articulation point
low(E)= 4 ≥ num(D)=4
Vertex C is an articulation point
low(G)= 7 ≥ num(C)=3
13
CE 222-Data Structures & Algorithms II,
Izmir University of Economics
14. 1) Computing num(v)
• A preorder
traversal to
compute
num(v).
CE 222-Data Structures & Algorithms II,
Izmir University of Economics
14
15. 2) Computing low(v)
• A postorder
traversal to
compute
low(v).
• To save a third
traversal for
finding
articulation
points, it is also
incorporated
into the second
pass.
CE 222-Data Structures & Algorithms II,
Izmir University of Economics
15
16. Articulation point algorithm
(All phases combined)
16
Do not forget to study Figures 9.65, 9.66, and 9.67! (assigning
num(v) and low(v) and finding articulation points)
CE 222-Data Structures & Algorithms II,
Izmir University of Economics