Graph data structure and algorithms

Nov 16, 2018Download as pptx, pdf

3 likes3,245 views

This document discusses graph data structures and algorithms. A graph consists of nodes and edges, where nodes represent entities and edges represent relationships between nodes. There are different types of graphs including undirected, directed, weighted, and cyclic graphs. Graphs can be represented using an adjacency matrix or adjacency list. Graphs are used to model real-world networks and solve problems in areas like social networks, maps, robot path planning, and neural networks.

Graph Data Structure and
Algorithms
Presented by
D.Anandhasilambarasan
KIT - CBE

Graph Data Structure and Algorithms
A Graph is a non-linear data structure consisting of nodes and
edges.
The nodes are sometimes also referred to as vertices and the
edges are lines or arcs that connect any two nodes in the graph.
A Graph consists of a finite set of vertices (or nodes) and set of
Edges which connect a pair of nodes.

Most read

Applications of Graphs
 Graphs are used to solve many real-life problems. Graphs are used to
represent networks.
 The networks may include paths in a city or telephone network or circuit
network.
 Graphs are also used in social networks like LinkedIn, Facebook.
 It is used to Document link graphs.
 Scene graphs: Such graphs are very important in the computer games
industry.

 Robot planning: Such graph plans are used in planning paths for
Autonomous vehicles.
 Neural networks
 For example, in Facebook, each person is represented with a vertex (or
node).
 Each node is a structure and contains information like person id, name,
gender, locale etc.

 Nodes: These are the most important components in any graph. Nodes are
entities whose relationships are expressed using edges. If a graph comprises
2 nodes A and B and an undirected edge between them, then it expresses a
bi-directional relationship between the nodes and edge.
 Edges: Edges are the components that are used to represent the relationships
between various nodes in a graph. An edge between two nodes expresses a
one-way or two-way relationship between the nodes.

- In the above Graph, the set of vertices V = {0,1,2,3,4}
and the set of edges E = {01, 12, 23, 34, 04, 14, 13}.

Types of graphs
Undirected: An undirected graph is a graph in which all the edges are bi-
directional
i.e. the edges do not point in any specific direction.
Directed: A directed graph is a graph in which all the edges are uni-directional
i.e. the edges point in a single direction.
Weighted: In a weighted graph, each edge is assigned a weight or cost.
Cyclic: A graph is cyclic if the graph comprises a path that starts from a vertex
and ends at the same vertex. That path is called a cycle. An acyclic graph is a
graph that has no cycle.

Graph Representation
You can represent a graph in many ways. The two most common ways of
representing a graph is as follows:
Adjacency matrix
Adjacency list
Adjacency matrix
An adjacency matrix is a VxV binary matrix A. Element Ai,j is 1 if there is an
edge from vertex i to vertex j else Ai,jis 0.
Note: A binary matrix is a matrix in which the cells can have only one of two
possible values - either a 0 or 1.

The adjacency matrix of the following graph is:
i/j: 1 2 3 4
1: 0 1 0 1
2: 1 0 1 0
3: 0 1 0 1
4: 1 0 1 0
Adjacency list
The other way to represent a graph is by using an adjacency list. An adjacency
list is an array A of separate lists. Each element of the array Ai is a list, which
contains all the vertices that are adjacent to vertex i.
1 2
34

This document defines and provides examples of graphs and their representations. It discusses: - Graphs are data structures consisting of nodes and edges connecting nodes. - Examples of directed and undirected graphs are given. - Graphs can be represented using adjacency matrices or adjacency lists. Adjacency matrices store connections in a grid and adjacency lists store connections as linked lists. - Key graph terms are defined such as vertices, edges, paths, and degrees. Properties like connectivity and completeness are also discussed.

Graph traversals in Data StructuresAnandhasilambarasan D

Graph traversal techniques are used to search vertices in a graph and determine the order to visit vertices. There are two main techniques: breadth-first search (BFS) and depth-first search (DFS). BFS uses a queue and visits the nearest vertices first, producing a spanning tree. DFS uses a stack and visits vertices by going as deep as possible first, also producing a spanning tree. Both techniques involve marking visited vertices to avoid loops.

Queue pptSouravKumar328

The document discusses different types of queues, including simple, circular, priority, and double-ended queues. It describes the basic queue operations of enqueue and dequeue, where new elements are added to the rear of the queue and existing elements are removed from the front. Circular queues are more memory efficient than linear queues by connecting the last queue element back to the first, forming a circle. Priority queues remove elements based on priority rather than order of insertion. Double-ended queues allow insertion and removal from both ends. Common applications of queues include CPU and disk scheduling, synchronization between asynchronous processes, and call center phone systems.

Directory implementation and allocation methodssangrampatil81

Stepper motor pptTayagarDiwakar

The document discusses stepper motors, including their principle of operation, classification, systems, advantages, and applications. A stepper motor divides a full rotation into steps using magnetic attraction between a rotor and stator. There are three main types - permanent magnet, variable reluctance, and hybrid motors. A stepper motor system consists of an indexer to generate pulses, a driver to power the motor, and the stepper motor itself. Stepper motors offer advantages like high torque, precision positioning, simplicity, and reliability. They are widely used in applications like drives, robotics, industrial machines, security, and medical devices.

Capgemini resume templateAkash gupta

This resume summarizes an individual's professional experience, including their title, location, languages spoken, experience summary, key skills, work history across multiple industries and roles, training and certifications, published works, and education. The resume lists the person's functional and technical skills, experience in various roles and industries without client or project names, responsibilities for each position, operating systems used, software and languages worked with, and special tools utilized. It also includes sections for training, certifications, publications, presentations, awards, and higher education.

STACKS IN DATASTRUCTUREArchie Jamwal

This document discusses stacks as a linear data structure. It defines a stack as a last-in, first-out (LIFO) collection where the last item added is the first removed. The core stack operations of push and pop are introduced, along with algorithms to insert, delete, and display items in a stack. Examples of stack applications include reversing strings, checking validity of expressions with nested parentheses, and converting infix notation to postfix.

Shortest path algorithmsana younas

The document discusses several shortest path algorithms for graphs, including Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm. Dijkstra's algorithm finds the shortest path from a single source node to all other nodes in a graph with non-negative edge weights. Bellman-Ford can handle graphs with negative edge weights but is slower. Floyd-Warshall can find shortest paths in a graph between all pairs of nodes.

Data structure - GraphMadhu Bala

The document defines and describes various graph concepts and data structures used to represent graphs. It defines a graph as a collection of nodes and edges, and distinguishes between directed and undirected graphs. It then describes common graph terminology like adjacent/incident nodes, subgraphs, paths, cycles, connected/strongly connected components, trees, and degrees. Finally, it discusses two common ways to represent graphs - the adjacency matrix and adjacency list representations, noting their storage requirements and ability to add/remove nodes.

Graphs In Data StructureAnuj Modi

A graph G is composed of vertices V connected by edges E. It can be represented using an adjacency matrix or adjacency lists. Graph search algorithms like depth-first search (DFS) and breadth-first search (BFS) are used to traverse the graph and find paths between vertices. DFS recursively explores edges until reaching the end of a branch before backtracking, while BFS explores all neighbors at each level before moving to the next.

Graph representationTech_MX

A graph consists of vertices and edges, where vertices represent entities and edges represent relationships between vertices. Graphs can be represented sequentially using matrices like adjacency and incidence matrices, or linked using data structures like adjacency lists. Adjacency matrices allow fast addition/removal of edges but use more memory, while adjacency lists use less memory but are slower to modify. The best representation depends on whether the graph is dense or sparse.

single linked listSathasivam Rangasamy

This document discusses the implementation of a single linked list data structure. It describes the nodes that make up a linked list, which have an info field to store data and a next field pointing to the next node. The document outlines different ways to represent linked lists, including static arrays and dynamic pointers. It also provides algorithms for common linked list operations like traversing, inserting, and deleting nodes from the beginning, end, or a specified position within the list.

HeapsHafiz Atif Amin

This document provides information about priority queues and binary heaps. It defines a binary heap as a nearly complete binary tree where the root node has the maximum/minimum value. It describes heap operations like insertion, deletion of max/min, and increasing/decreasing keys. The time complexity of these operations is O(log n). Heapsort, which uses a heap data structure, is also covered and has overall time complexity of O(n log n). Binary heaps are often used to implement priority queues and for algorithms like Dijkstra's and Prim's.

Divide and conquer - Quick sortMadhu Bala

AVL Tree in Data Structure Vrushali Dhanokar

This document discusses AVL trees, which are height-balanced binary search trees. It defines AVL trees, explains why they are useful by comparing insertion performance to regular binary search trees, and covers balance factors, rotations, and the insertion algorithm. Key points made include that AVL trees have logarithmic time complexity for operations through self-balancing, and maintain an extra balance factor field for each node. Various example questions related to building AVL trees from data are also provided.

AVL TreeDr Sandeep Kumar Poonia

Binary Search Treesagar yadav

Binary search trees are binary trees where all left descendants of a node are less than the node's value and all right descendants are greater. This structure allows for efficient search, insertion, and deletion operations. The document provides definitions and examples of binary search tree properties and operations like creation, traversal, searching, insertion, deletion, and finding minimum and maximum values. Applications include dynamically maintaining a sorted dataset to enable efficient search, insertion, and deletion.

HashingAmar Jukuntla

Hashing is a technique used to uniquely identify objects by assigning each object a key, such as a student ID or book ID number. A hash function converts large keys into smaller keys that are used as indices in a hash table, allowing for fast lookup of objects in O(1) time. Collisions, where two different keys hash to the same index, are resolved using techniques like separate chaining or linear probing. Common applications of hashing include databases, caches, and object representation in programming languages.

Terminology of treeRacksaviR

Bfs and DfsMasud Parvaze

Lecture 16 memory bounded searchHema Kashyap

RBFS is a memory bounded search algorithm similar to A* that uses heuristics. It recursively searches the problem space, but only keeps the current search path and sibling nodes in memory at a given time. When exploring a subtree no longer looks promising, it is abandoned to save space. The space complexity of RBFS is linear in the search depth, same as IDA*. RBFS inherits heuristic values h(n) from parent nodes to child nodes to guide the search when reexploring subtrees.

linked list in data structure shameen khan

This document discusses data structures and linked lists. It provides definitions and examples of different types of linked lists, including: - Single linked lists, which contain nodes with a data field and a link to the next node. - Circular linked lists, where the last node links back to the first node, forming a loop. - Doubly linked lists, where each node contains links to both the previous and next nodes. - Operations on linked lists such as insertion, deletion, traversal, and searching are also described.

Spanning treesShareb Ismaeel

Tree and graphMuhaiminul Islam

The document defines trees and graphs, their terminology, and applications. It describes trees as undirected graphs where any two vertices are connected by a single path. Binary search trees allow for fast insertion and removal and are designed for fast searching. Trees can be traversed in pre-order, in-order, and post-order. They have applications in file systems, AI, compiler design, and text processing. Graphs consist of nodes and edges, and can be directed or undirected. Graphs have applications in maps, networks, VLSI design, and the internet.

Threaded Binary Tree.pptxpavankumarjakkepalli

A threaded binary tree is a variant of a binary tree that stores in-order predecessor and successor pointers instead of null pointers to facilitate faster traversal without recursion or an auxiliary stack. There are two types: single-threaded stores successor pointers only, and double-threaded stores both predecessor and successor pointers. This decreases wasted space from null pointers but makes insertion and deletion more complex. Advantages include optimal memory usage and faster traversal, while disadvantages include more complex insertion/deletion and slightly more memory usage for thread indicators.

Algorithms Lecture 7: Graph AlgorithmsMohamed Loey

Priority queue in DSAjunnubabu

The document discusses priority queues, which are data structures that allow elements to be inserted and removed based on priority. Elements with higher priority are served before those with lower priority. There are two main types - ascending order queues prioritize lower numbers, while descending order queues prioritize higher numbers. Priority queues can be implemented using linked lists, arrays, binary heaps, and binary search trees. Common applications include shortest path algorithms, heap sorting, and operating system processes.

Prim's Algorithm on minimum spanning treeoneous

Prim's algorithm is used to find the minimum spanning tree of a connected, undirected graph. It works by continuously adding edges to a growing tree that connects vertices. The algorithm maintains two lists - a closed list of vertices already included in the minimum spanning tree, and a priority queue of open vertices. It starts with a single vertex in the closed list. Then it selects the lowest cost edge that connects an open vertex to a closed one, adds it to the tree and updates the lists. This process repeats until all vertices are in the closed list and connected by edges in the minimum spanning tree. The algorithm runs in O(E log V) time when using a binary heap priority queue.

Graphs in datastructuresLikhithaGunturi

Graphs are a non-linear data structure consisting of vertices and edges. Vertices are represented by circles or nodes, and edges represent the connections between vertices. A graph shows the relationships between data. Graphs can be directed or undirected, weighted or unweighted, and cyclic or acyclic. Graphs are commonly represented using adjacency matrices or adjacency lists and have many applications including modeling electrical circuits, project planning, mathematics, computer networks, and finding shortest routes.

Graphs in data structurehamza javed

Graphs are a data structure composed of nodes connected by edges. There are two main types: directed graphs where edges show a flow between nodes, and undirected graphs where edges simply show a relationship between nodes. Key terminology includes adjacent nodes, paths, cyclic vs acyclic paths, and representations like adjacency matrices and lists. Graphs can model many real-world applications such as social networks, computer networks, road maps, and more.

More Related Content

What's hot (20)

Data structure - GraphMadhu Bala

Graphs In Data StructureAnuj Modi

Graph representationTech_MX

single linked listSathasivam Rangasamy

HeapsHafiz Atif Amin

Divide and conquer - Quick sortMadhu Bala

AVL Tree in Data Structure Vrushali Dhanokar

AVL TreeDr Sandeep Kumar Poonia

Binary Search Treesagar yadav

HashingAmar Jukuntla

Terminology of treeRacksaviR

Bfs and DfsMasud Parvaze

Lecture 16 memory bounded searchHema Kashyap

linked list in data structure shameen khan

Spanning treesShareb Ismaeel

Tree and graphMuhaiminul Islam

Threaded Binary Tree.pptxpavankumarjakkepalli

Algorithms Lecture 7: Graph AlgorithmsMohamed Loey

Priority queue in DSAjunnubabu

Prim's Algorithm on minimum spanning treeoneous

Data structure - GraphMadhu Bala

Graphs In Data StructureAnuj Modi

Graph representationTech_MX

single linked listSathasivam Rangasamy

HeapsHafiz Atif Amin

Divide and conquer - Quick sortMadhu Bala

AVL Tree in Data Structure Vrushali Dhanokar

AVL TreeDr Sandeep Kumar Poonia

Binary Search Treesagar yadav

HashingAmar Jukuntla

Terminology of treeRacksaviR

Bfs and DfsMasud Parvaze

Lecture 16 memory bounded searchHema Kashyap

linked list in data structure shameen khan

Spanning treesShareb Ismaeel

Tree and graphMuhaiminul Islam

Threaded Binary Tree.pptxpavankumarjakkepalli

Algorithms Lecture 7: Graph AlgorithmsMohamed Loey

Priority queue in DSAjunnubabu

Prim's Algorithm on minimum spanning treeoneous

Similar to Graph data structure and algorithms (20)

Graphs in datastructuresLikhithaGunturi

Graphs in data structurehamza javed

Graphs data structuresJasleen Kaur (Chandigarh University)

Data Structure and algorithms - Graph1.pptxKishor767966

graphs presentation .pptxssuser800a48

A graph G consists of a set of vertices V connected by edges E. It can be represented by an adjacency matrix with 1s indicating edges and 0s for no edge. A graph has applications in networks and electronics. Key terminology includes adjacency, degree, paths, and cycles. There are different types of graphs such as finite, infinite, trivial, simple, null, complete, and disconnected graphs. Graphs can be represented using an adjacency matrix or adjacency list.

Basics of graphKhaled Sany

This document discusses graphs and their applications and representations. It defines what a graph is consisting of vertices and edges. It describes different types of graphs such as trees, connected graphs, weighted graphs, directed graphs, and multigraphs. It also discusses graph representations including adjacency matrices and adjacency lists. Finally, it notes that adjacency lists are generally preferred for sparse graphs while adjacency matrices are better for dense graphs.

Lecture 5b graphs and hashingVictor Palmar

This document provides definitions and concepts related to graphs. It defines a graph as a data structure consisting of vertices and edges linking vertices, which can represent objects and relationships. Graphs are a generalization of trees that allow any type of relationship between nodes. Common graph representations include adjacency lists and matrices. The document also discusses graph traversal methods, minimum spanning trees, and applications of graphs.

Unit II_Graph.pptxkgjrekjgiojtoiejhgnltegjtepournima055

The document discusses graphs and graph algorithms. It covers basic graph concepts like vertices, edges, paths, cycles. It describes different ways to represent graphs like adjacency matrix, adjacency list and their pros and cons. It also discusses operations on graphs like inserting and deleting vertices and edges. The document explains traversal algorithms like depth-first search and breadth-first search. It covers minimum spanning tree algorithms like Prim's and Kruskal's. It also briefly discusses shortest path algorithms like Dijkstra's and topological sorting.

graph representation.pdfamitbhachne

A graph is a data structure consisting of vertices and edges. Graphs are used to represent real-world networks like social networks and road maps. There are two main representations of graphs: adjacency matrix and adjacency list. Adjacency matrix uses a 2D array to store edge information while adjacency list uses an array of lists. Graph traversal algorithms like depth-first search and breadth-first search are used to search graphs in a systematic way without loops.

Graph Basic In Data structureIkhlas Rahman

The document provides an overview of graph concepts including definitions of a graph as a pair of vertices and edges, different types of graphs such as directed/undirected graphs and cyclic/acyclic graphs. It also discusses graph coloring, adjacency matrices/lists for representing graphs, and algorithms for traversing graphs including depth-first search and breadth-first search. Examples are given for each concept to illustrate key differences and properties.

Techniques for graphSOWNDARYASUGCOMPUTER

This document discusses data structures and analytics related to graphs. It defines what a graph is, including that it consists of vertices and edges. Vertices are also called nodes, and edges connect pairs of nodes. The document outlines the basic components of graphs, including vertices and edges. It then lists various topics related to graphs that are covered, such as graph representations, types of graphs with examples, graph properties, and applications of graphs. Breadth-first traversal, depth-first traversal, and detecting cycles in graphs are also mentioned.

DATA STRUCTURES unit 4.pptxShivamKrPathak

Graphs in Data Structurehafsa komal

The document discusses graphs and their applications. It defines graphs as consisting of vertices and edges. Graphs can be undirected or directed. It also defines concepts like paths, cycles, and connectivity in graphs. Common applications of graphs mentioned are maps, computer networks, and CAD designs. The document then discusses different ways to represent graphs using adjacency matrices and adjacency lists. It also discusses using graphs to model and solve problems like traffic light timing by coloring the vertices.

Lecture 14 data structures and algorithmsAakash deep Singhal

The document describes graphs and graph algorithms. It defines what a graph is composed of (vertices and edges) and different types of graphs like directed and undirected graphs. It provides terminology used with graphs like vertices, edges, paths, cycles, connectedness. It discusses ways of representing graphs through adjacency matrices and adjacency lists and provides examples. It also describes Dijkstra's algorithm, a graph algorithm to find the shortest path between vertices in a graph.

Graph theory ppt.pptxsaranyajey

The document defines a graph and discusses its history, types, representations, and applications. It begins with Leonhard Euler's solving of the Königsberg bridge problem in 1735, which is considered the first theorem of graph theory. A graph is defined as a pair of sets (V,E) where V is the set of vertices and E is the set of edges. Graphs can have parallel edges, loops, directed or undirected edges. They can be represented through adjacency matrices or lists. Applications of graph theory include computer science, physics/chemistry, and computer networks.

Chapter 6-DS(Introduction to Graph and its terminologies).pptxnasalapurepallavi272

graphs in data structure for Btech students.pdfsoniasharmafdp

UNIT IV NON LINEAR DATA STRUCTURES - GRAPHVISWANATHAN R V

UNIT IV NON LINEAR DATA STRUCTURES - GRAPH.pptxkncetaruna

Chap 6 Graph.pptshashankbhadouria4

This document defines and provides examples of graphs and graph representations. It begins by discussing Euler's use of graphs to solve the Königsberg bridge problem. It then defines graphs formally as G=(V,E) with vertices V and edges E. Examples of undirected and directed graphs are given. Common graph terms like paths, cycles, and connectivity are defined. Methods of representing graphs through adjacency matrices and adjacency lists are described and examples are provided. Finally, the document briefly discusses graph traversal algorithms and the concept of weighted edges.

Graphs in datastructuresLikhithaGunturi

Graphs in data structurehamza javed

Graphs data structuresJasleen Kaur (Chandigarh University)

Data Structure and algorithms - Graph1.pptxKishor767966

graphs presentation .pptxssuser800a48

Basics of graphKhaled Sany

Lecture 5b graphs and hashingVictor Palmar

Unit II_Graph.pptxkgjrekjgiojtoiejhgnltegjtepournima055

graph representation.pdfamitbhachne

Graph Basic In Data structureIkhlas Rahman

Techniques for graphSOWNDARYASUGCOMPUTER

DATA STRUCTURES unit 4.pptxShivamKrPathak

Graphs in Data Structurehafsa komal

Lecture 14 data structures and algorithmsAakash deep Singhal

Graph theory ppt.pptxsaranyajey

Chapter 6-DS(Introduction to Graph and its terminologies).pptxnasalapurepallavi272

graphs in data structure for Btech students.pdfsoniasharmafdp

UNIT IV NON LINEAR DATA STRUCTURES - GRAPHVISWANATHAN R V

UNIT IV NON LINEAR DATA STRUCTURES - GRAPH.pptxkncetaruna

Chap 6 Graph.pptshashankbhadouria4

Recently uploaded (20)

june 10 2025 ppt for madden on art science is over.pptxroger malina

Ray Dalio How Countries go Broke the Big CycleDadang Solihin

A complete and practical understanding of the Big Debt Cycle. A much more practical understanding of how supply and demand really work compared to the conventional economic thinking. A complete and practical understanding of the Overall Big Cycle, which is driven by the Big Debt Cycle and the other major cycles, including the big political cycle within countries that changes political orders and the big geopolitical cycle that changes world orders.

Parenting Teens: Supporting Trust, resilience and independencePooky Knightsmith

For more information about my speaking and training work, visit: https://www.pookyknightsmith.com/speaking/ SESSION OVERVIEW: Parenting Teens: Supporting Trust, Resilience & Independence The teenage years bring new challenges—for teens and for you. In this practical session, we’ll explore how to support your teen through emotional ups and downs, growing independence, and the pressures of school and social life. You’ll gain insights into the teenage brain and why boundary-pushing is part of healthy development, along with tools to keep communication open, build trust, and support emotional resilience. Expect honest ideas, relatable examples, and space to connect with other parents. By the end of this session, you will: • Understand how teenage brain development affects behaviour and emotions • Learn ways to keep communication open and supportive • Explore tools to help your teen manage stress and bounce back from setbacks • Reflect on how to encourage independence while staying connected • Discover simple strategies to support emotional wellbeing • Share experiences and ideas with other parents

How to Create a Rainbow Man Effect in Odoo 18Celine George

Unit- 4 Biostatistics & Research Methodology.pdfKRUTIKA CHANNE

Blocking and confounding (when a third variable, or confounder, influences both the exposure and the outcome) system for Two-level factorials (a type of experimental design where each factor (independent variable) is investigated at only two levels, typically denoted as "high" and "low" or "+1" and "-1") Regression modeling (statistical model that estimates the relationship between one dependent variable and one or more independent variables using a line): Hypothesis testing in Simple and Multiple regression models Introduction to Practical components of Industrial and Clinical Trials Problems: Statistical Analysis Using Excel, SPSS, MINITAB®️, DESIGN OF EXPERIMENTS, R - Online Statistical Software to Industrial and Clinical trial approach

$Basic English for Communication - Dr Hj Euis Eti Rohaeti Mpd$ $Basic English for Communication - Dr Hj Euis Eti Rohaeti Mpd$

Basic English for Communication - Dr Hj Euis Eti Rohaeti MpdRestu Bias Primandhika

Different pricelists for different shops in odoo Point of Sale in Odoo 17Celine George

Final Sketch Designs for poster production.pptxbobby205207

Allomorps and word formation.pptx - Google Slides.pdfAbha Pandey

IDF 30min presentation - December 2, 2024.pptxArneeAgligar

How to Configure Vendor Management in Lunch App of Odoo 18Celine George

FEBA Sofia Univercity final diplian v3 GSDG 5.2025.pdfChristinaFortunova

How to Manage & Create a New Department in Odoo 18 EmployeeCeline George

Diptera: The Two-Winged Wonders, The Fly Squad: Order Diptera.pptxArshad Shaikh

Diptera, commonly known as flies, is a large and diverse order of insects that includes mosquitoes, midges, gnats, and horseflies. Characterized by a single pair of wings (hindwings are modified into balancing organs called halteres), Diptera are found in almost every environment and play important roles in ecosystems as pollinators, decomposers, and food sources. Some species, however, are significant pests and disease vectors, transmitting diseases like malaria, dengue, and Zika virus.

BUSINESS QUIZ PRELIMS | QUIZ CLUB OF PSGCAS | 9 SEPTEMBER 2024Quiz Club of PSG College of Arts & Science

EUPHORIA GENERAL QUIZ FINALS | QUIZ CLUB OF PSGCAS | 21 MARCH 2025Quiz Club of PSG College of Arts & Science

How to Manage Maintenance Request in Odoo 18Celine George

Black and White Illustrative Group Project Presentation.pdf (1).pdfAnnasofiaUrsini

SEXUALITY , UNWANTED PREGANCY AND SEXUAL ASSAULT .pptxPoojaSen20

LDMMIA Free Reiki Yoga S9 Grad Level Intuition IILDM & Mia eStudios

Completed Sunday 6/8. For Weekend 6/14 & 15th. (Fathers Day Weekend US.) These workshops are also timeless for future students TY. No admissions needed. A 9th FREE WORKSHOP Reiki - Yoga “Intuition-II, The Chakras” Your Attendance is valued. We hit over 5k views for Spring Workshops and Updates-TY. Thank you for attending our workshops. If you are new, do welcome. Grad Students: I am planning a Reiki-Yoga Master Course (As a package). I’m Fusing both together. This will include the foundation of each practice. Our Free Workshops can be used with any Reiki Yoga training package. Traditional Reiki does host rules and ethics. Its silent and within the JP Culture/Area/Training/Word of Mouth. It allows remote healing but there’s limits As practitioners and masters, we are not allowed to share certain secrets/tools. Some content is designed only for “Masters”. Some yoga are similar like the Kriya Yoga-Church (Vowed Lessons). We will review both Reiki and Yoga (Master tools) in the Course upcoming. S9/This Week’s Focus: * A continuation of Intuition-2 Development. We will review the Chakra System - Our temple. A misguided, misused situation lol. This will also serve Attunement later. Thx for tuning in. Your time investment is valued. I do select topics related to our timeline and community. For those seeking upgrades or Reiki Levels. Stay tuned for our June packages. It’s for self employed/Practitioners/Coaches… Review & Topics: * Reiki Is Japanese Energy Healing used Globally. * Yoga is over 5k years old from India. It hosts many styles, teacher versions, and it’s Mainstream now vs decades ago. * Anything of the Holistic, Wellness Department can be fused together. My origins are Alternative, Complementary Medicine. In short, I call this ND. I am also a metaphysician. I learnt during the 90s New Age Era. I forget we just hit another wavy. It’s GenZ word of Mouth, their New Age Era. WHOA, History Repeats lol. We are fusing together. * So, most of you have experienced your Spiritual Awakening. However; The journey wont be perfect. There will be some roller coaster events. The perks are: We are in a faster Spiritual Zone than the 90s. There’s more support and information available. (See Presentation for all sections, THX AGAIN.)

june 10 2025 ppt for madden on art science is over.pptxroger malina

Ray Dalio How Countries go Broke the Big CycleDadang Solihin

Parenting Teens: Supporting Trust, resilience and independencePooky Knightsmith

How to Create a Rainbow Man Effect in Odoo 18Celine George

Unit- 4 Biostatistics & Research Methodology.pdfKRUTIKA CHANNE

$Basic English for Communication - Dr Hj Euis Eti Rohaeti Mpd$ $Basic English for Communication - Dr Hj Euis Eti Rohaeti Mpd$

Basic English for Communication - Dr Hj Euis Eti Rohaeti MpdRestu Bias Primandhika

Different pricelists for different shops in odoo Point of Sale in Odoo 17Celine George

Final Sketch Designs for poster production.pptxbobby205207

Allomorps and word formation.pptx - Google Slides.pdfAbha Pandey

IDF 30min presentation - December 2, 2024.pptxArneeAgligar

How to Configure Vendor Management in Lunch App of Odoo 18Celine George

FEBA Sofia Univercity final diplian v3 GSDG 5.2025.pdfChristinaFortunova

How to Manage & Create a New Department in Odoo 18 EmployeeCeline George

Diptera: The Two-Winged Wonders, The Fly Squad: Order Diptera.pptxArshad Shaikh

BUSINESS QUIZ PRELIMS | QUIZ CLUB OF PSGCAS | 9 SEPTEMBER 2024Quiz Club of PSG College of Arts & Science

EUPHORIA GENERAL QUIZ FINALS | QUIZ CLUB OF PSGCAS | 21 MARCH 2025Quiz Club of PSG College of Arts & Science

How to Manage Maintenance Request in Odoo 18Celine George

Black and White Illustrative Group Project Presentation.pdf (1).pdfAnnasofiaUrsini

SEXUALITY , UNWANTED PREGANCY AND SEXUAL ASSAULT .pptxPoojaSen20

LDMMIA Free Reiki Yoga S9 Grad Level Intuition IILDM & Mia eStudios

Graph data structure and algorithms

1. Graph Data Structure and Algorithms Presented by D.Anandhasilambarasan KIT - CBE

2. Graph Data Structure and Algorithms A Graph is a non-linear data structure consisting of nodes and edges. The nodes are sometimes also referred to as vertices and the edges are lines or arcs that connect any two nodes in the graph. A Graph consists of a finite set of vertices (or nodes) and set of Edges which connect a pair of nodes.

3.  Nodes: These are the most important components in any graph. Nodes are entities whose relationships are expressed using edges. If a graph comprises 2 nodes A and B and an undirected edge between them, then it expresses a bi-directional relationship between the nodes and edge.  Edges: Edges are the components that are used to represent the relationships between various nodes in a graph. An edge between two nodes expresses a one-way or two-way relationship between the nodes.

4. - In the above Graph, the set of vertices V = {0,1,2,3,4} and the set of edges E = {01, 12, 23, 34, 04, 14, 13}.

5. Types of graphs Undirected: An undirected graph is a graph in which all the edges are bi- directional i.e. the edges do not point in any specific direction. Directed: A directed graph is a graph in which all the edges are uni-directional i.e. the edges point in a single direction. Weighted: In a weighted graph, each edge is assigned a weight or cost. Cyclic: A graph is cyclic if the graph comprises a path that starts from a vertex and ends at the same vertex. That path is called a cycle. An acyclic graph is a graph that has no cycle.

6. Graph Representation You can represent a graph in many ways. The two most common ways of representing a graph is as follows: Adjacency matrix Adjacency list Adjacency matrix An adjacency matrix is a VxV binary matrix A. Element Ai,j is 1 if there is an edge from vertex i to vertex j else Ai,jis 0. Note: A binary matrix is a matrix in which the cells can have only one of two possible values - either a 0 or 1.

7. The adjacency matrix of the following graph is: i/j: 1 2 3 4 1: 0 1 0 1 2: 1 0 1 0 3: 0 1 0 1 4: 1 0 1 0 Adjacency list The other way to represent a graph is by using an adjacency list. An adjacency list is an array A of separate lists. Each element of the array Ai is a list, which contains all the vertices that are adjacent to vertex i. 1 2 34

9. Applications of Graphs  Graphs are used to solve many real-life problems. Graphs are used to represent networks.  The networks may include paths in a city or telephone network or circuit network.  Graphs are also used in social networks like LinkedIn, Facebook.  It is used to Document link graphs.  Scene graphs: Such graphs are very important in the computer games industry.

10.  Robot planning: Such graph plans are used in planning paths for Autonomous vehicles.  Neural networks  For example, in Facebook, each person is represented with a vertex (or node).  Each node is a structure and contains information like person id, name, gender, locale etc.

Graph data structure and algorithms

Recommended

More Related Content

What's hot (20)

Similar to Graph data structure and algorithms (20)

Recently uploaded (20)

Graph data structure and algorithms