Bi-Partie Graph

Duration: 4 min

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  1. General Intelligence & Reasoning: Analogy, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Series (Number and Letter Series) (Numerical Relations and Reasoning), Coding Decoding, Blood Relations, Directions (Direction Test), Ranking, Order and Sequence, Logical Sequence Of Words, Syllogisms, Venn Diagram, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Selection Decision Table (Decision Making), Data Sufficiency, Puzzle Solving (Puzzles), Data Arrangement, Cubes & Dices, Calendar, Clock, Arithmetic, Number System, HCF LCM, Verbal Analogy (Word Based Analogy), Visual Recognition and Figure Completion, Paper Folding and Cutting, Transformation of Shapes, Clocks and Calendars, Direction Sense, Cubes and Dice, Patterns in Two-Dimensional Figures, Seating Arrangements, Analogy and Classification, Logical Deduction and Induction, Alphabet and Word Tests, Mathematical Operations, Series Completion, Simplification and Approximation, Coding and Decoding, Numerical Reasoning, Assembling and Grouping of Shapes, Three-Dimensional Patterns and Visualization, Vocabulary, Number System, Averages, Mixtures and Alligation, Simple and Compound Interest, Percentages, Algebra, Speed, Time and Distance, Seating and Arrangement Puzzles, Traverse Surveying, Order, Ranking and Sequence, Constraint Puzzles, Syllogisms and Venn Diagrams, Reading Comprehension, Critical Reasoning, Permutations and Combinations, Narrative Sequencing, Project Monitoring, Resources and Safety, Profit, Loss and Discount, Ratio and Proportion, Time and Work, Mensuration, Data Interpretation, Powers, Surds and Logarithms, Sequences and Series, Articles and Determiners, Sentence Completion, Data Sufficiency, Blood Relations, Simple Interest and Compound Interest, Mixture and Alligation, Sequence and Series (Series), Speed Time and Distance, Algebra, Profit and Loss, Powers and Exponents (Surds and Indices), Permutation and Combination
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  5. Computer Organisation & Architecture: Basics of COA, Cache Memory Organization, Input Output Organisation, Pipelining, Instr Formats & Modes, Control Unit Design, Programming the Basic Computer, Register Transfer and Microoperations, Semiconductor Memories and Programmable Logic, Processor Organization and Instructions, Memory Hierarchy and Input/Output, Registers and Counters
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  7. Databases: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, SQL, Relational Calculus, Transaction Management, Concurrency Control, Database Recovery, Database Security & Authorization
  8. Algorithms: Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos
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  13. Full Length Mock Tests:
  14. Sectional Tests:
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video lecture covers the definitions and properties of Bi-partite and Complete Bi-partite graphs. The instructor defines a Bi-partite graph as one where vertices are partitioned into two disjoint sets such that edges only connect vertices from different sets. He then defines a Complete Bi-partite graph where every vertex in one set connects to every vertex in the other, denoted as $K_{m,n}$. The lecture includes visual demonstrations and formula derivations for edge counts.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The instructor begins by defining a **Bi-partite graph**. He reads the formal definition from the slide: "A graph G(V, E) is called bi-partite graph if it's vertex set V(G) can be partitioned into two non-empty disjoint subset V1(G) and V2(G) in such a way that each edge e in E(G) has it's one end point in V1(g) and other end point in V2(g)." He emphasizes the condition that edges must connect vertices from different sets, not within the same set. He underlines key terms like "partitioned", "non-empty disjoint subset", and "one end point". He then illustrates this with a diagram, circling the left column of red vertices as set V1 and the right column of black vertices as set V2. He explains that the partition $V = V1 \cup V2$ is called the bipartition of G. He then introduces the second concept, **Complete Bi-partite graph**, reading the definition: "A Bi-partite graph G(V, E) is called Complete bi-partite graph if every vertex in the first partition is connected to every vertex in the second partition, denoted by $K_{m,n}$."

  2. 2:00 – 3:46 02:00-03:46

    The instructor elaborates on the **Complete Bi-partite graph** notation $K_{m,n}$. He writes $|V1| = m$ and $|V2| = n$ to represent the size of the two partitions. He explains that in a complete bipartite graph, the total number of edges is the product of the sizes of the two sets, writing $K_{m,n} = m imes n$. He draws a square graph with vertices labeled 1, 2, 3, 4 to demonstrate a bipartite structure, circling vertices 1 and 3 as one set and 2 and 4 as the other. He then draws a specific example of a complete bipartite graph with 3 vertices on the left and 3 on the right, labeling it $K_{3,3}$. He draws lines connecting every left vertex to every right vertex to show the "complete" nature. He also draws a graph with 4 vertices (1, 2, 3, 4) in a cycle, circling 1 and 3, and 2 and 4 to show it is bipartite. He emphasizes that for a graph to be bipartite, you must be able to divide vertices into two sets such that no two vertices within the same set are connected.

The lecture systematically builds the concept of bipartite graphs. It starts with the fundamental definition involving vertex partitioning and edge constraints. It then moves to the specific case of complete bipartite graphs, where connectivity is maximized between the two sets. The instructor uses visual aids, underlining text, and hand-drawn diagrams to reinforce the definitions. Key takeaways include the notation $K_{m,n}$, the condition for bipartition, and the formula for the number of edges in a complete bipartite graph ($m imes n$).

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