# Introduction to Graphs - EDITS
# Introduction
Graphs have widespread use within software engineering. They're a powerful data structure utilized to represent relationships between different types of data. In a graph, data points are stored as a **nodes** and each relationship between two data points is represented by an **edge**. For example, a social network is a graph in which people are nodes and friendships between two people are considered edges.
Graphs are best suited for problems in which there are binary relationships between objects. Once a problem can be represented as a graph, the problem can usually be solved using one of the key graph algorithms. For interviews, it is vital to know how to implement a graph, basic graph traversals (BFS, DFS) and how to topologically sort graph(s).
# Graph Terminology
## Graph components
Graphs consist of a set of..
* **vertices**, also referred to as nodes
* Nodes that are directly connected by an edge are commonly referred to as **neighbors**.
* **edges**, connections between pairs of vertices
<img src="https://lh3.google.com/u/0/d/1pjpXfzKUMLtCMbN-lajELJtxxouGmOYG=w2312-h1560-iv1" width="472" height="350"/>
## Graph types
### Directed & undirected graphs
A **directed** graph is a graph in which all edges have directions. An example of a directed edge is a one way street.
An **undirected** graph is a graph in which all edges do not have a direction. An example of this is a friendship!
<img src="https://lh3.google.com/u/0/d/1DFNg6q729fy_8HuufwSggfi6EzTGiBrs=w2312-h1560-iv1" width="486" height="380"/>
### Cyclic & acyclic graphs
Before reviewing what cyclic and acyclic graphs are, there are two key terms to cover: **paths** and **cycles**. A **path** is a sequence of vertices connected by edges and a **cycle** is a path whose first and last vertices are the same.
A **cyclic** graph contains at least one cycle.
An **acyclic** graph has no cycles.
<img src="https://lh3.google.com/u/0/d/1sGBAQhiOBf--NkM9cOAjAWAJ3d2LaUd2=w2312-h1560-iv1" width="486" height="380"/>
**Directed acylic graphs (DAGs)**, directed graphs without cycles, commonly appear in interview questions. In a DAG, the following terms are often used to denote nodes with special properties:
* **Sink** nodes have no outgoing edges, only incoming edges
* **Source** nodes have no incoming edges, only outgoing edges
# Graph representations
## Adjacency lists
An **adjacency list** is the most common way to represent a graph. In an adjacency list, each node stores a list of its adjacent vertices. For undirected graphs, each edge from _u_ to _v_ is stored twice: once in _u_'s list of neighbors and once in _v_'s list of neighbors.
<img src="https://lh3.google.com/u/0/d/1C_BKwFN-Uny1zkZIxN1tEtKoq9czJ__5=w2312-h1560-iv1" width="647" height="300"/>
## Edge sets/ lists
An **edge set** simply represents a graph as a collection of all its edges.
<img src="https://lh3.google.com/u/0/d/12yCXaO4bD97hoOzZ5TTX6ITW_RLbgF10=w2312-h1560-iv1" width="647" height="300"/>
## Adjacency matrix
An **adjacency matrix** represents a graph with _n_ nodes as a _n_ by _n_ boolean matrix, in which matrix[_u_][_v_] is set to true if an edge exists between node _u_ to node _v_.
<img src="https://lh3.google.com/u/0/d/1VBnJtc6nnYFA-qdkr2A8dXCA7eQkFWfp=w2312-h1560-iv1" width="685" height="350"/>
This representation of a graph is efficient for checking if an edge exists between a pair of vertices. However, it may be less efficient for search algorithms because it requires iterating through all the nodes in the graph to identify a node's neighbors.
## Runtime Analysis
Below is a chart of the most common graph operations and their runtimes for each of the graph representations. In the chart below, _V_ represents the number of verticies in the graph and _E_ represents the number of edges in the graph.
| Representation | Getting all adjacent edges for a vertex| Traversing entire graph | hasEdge(u, v) | Space |
| -------- | -------- | -------- | -------- | -------- |
| **Adjacency matrix** | O(V) | O(V<sup>2</sup>) | O(1) | O(V<sup>2</sup>)|
| **Edge Set** | O(E) | O(E) | O(E) | O(E)
| **Adjacency List** | O(1) | O(V + E) | O(max number of edges a vertex has) | O(E + V)
Credit: [UC Berkeley data structures course](https://docs.google.com/presentation/d/1GOOt1Ierm9jJFq9o26uRW20GdU6E5hrAZvsoQIreJew)

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