### Nội dung bài viết

# Graph

A **graph** organizes items in an interconnected network.

Each item is a **node** (or **vertex**). Nodes are connected by **edges**.

## Strengths

**Representing links**. Graphs are ideal for cases where you're working with*things that connect to other things*. Nodes and edges could, for example, respectively represent cities and highways, routers and ethernet cables, or Facebook users and their friendships.

## Weaknesses

**Scaling challenges**. Most graph algorithms are

O(n \log(n))

or even slower. Depending on the size of your graph, running algorithms across your nodes may not be feasible.

# Terminology

## Directed or undirected

In **directed** graphs, edges point from the node at one end to the node at the other end. In **undirected** graphs, the edges simply connect the nodes at each end.

## Cyclic or acyclic

A graph is **cyclic** if it has a cycle—an unbroken series of nodes with no repeating nodes or edges that connects back to itself. Graphs without cycles are **acyclic**.

## Weighted or unweighted

If a graph is **weighted**, each edge has a "weight." The weight could, for example, represent the distance between two locations, or the cost or time it takes to travel between the locations.

## Legal coloring

A **graph coloring** is when you assign colors to each node in a graph. A **legal coloring** means no adjacent nodes have the same color:

# Representations

There are a few different ways to store graphs. Let's take this graph as an example:

## Edge list

A list of all the edges in the graph:

`graph = [[1], [1], [1][2], [2]]`

Since node `3`

has edges to nodes `1`

and `2`

, `[1, 3]`

and `[2, 3]`

are in the edge list.

Sometimes it's helpful to pair our edge list with a list of all the *nodes*. For example, what if a node doesn't have *any* edges connected to it? It wouldn't show up in our edge list at all!

## Adjacency list

A list where the index represents the node and the value at that index is a list of the node's neighbors:

```
graph = [
[1],
[2],
[1][2],
[1],
]
```

Since node `3`

has edges to nodes `1`

and `2`

, `graph[3]`

has the adjacency list `[1, 2]`

.

We could also use a dictionary where the keys represent the node and the values are the lists of neighbors.

```
graph = {
0: [1],
1: [2],
2: [1][2],
3: [1],
}
```

This would be useful if the nodes were represented by strings, objects, or otherwise didn't map cleanly to list indices.

## Adjacency matrix

A matrix of `0`

s and `1`

s indicating whether node `x`

connects to node `y`

(`0`

means no, `1`

means yes).

```
graph = [
[1],
[1][1][1],
[1][1],
[1][1],
]
```

Since node `3`

has edges to nodes `1`

and `2`

, `graph[3][1]`

and `graph[3][2]`

have value `1`

.

# Algorithms

## BFS and DFS

You should know breadth-first search (BFS) and depth-first search (DFS) down pat so you can code them up quickly.

Lots of graph problems can be solved using just these traversals:

**Is there a path between two nodes in this undirected graph?** Run DFS or BFS from one node and see if you reach the other one.

**What's the shortest path between two nodes in this undirected, unweighted graph?** Run BFS from one node and backtrack once you reach the second. *Note:* BFS always finds the shortest path, assuming the graph is undirected and unweighted. DFS does *not* always find the shortest path.

**Can this undirected graph be colored with two colors?** Run BFS, assigning colors as nodes are visited. Abort if we ever try to assign a node a color different from the one it was assigned earlier.

**Does this undirected graph have a cycle?** Run BFS, keeping track of the number of times we're visiting each node. If we ever visit a node twice, then we have a cycle.

## Advanced graph algorithms

If you have lots of time before your interview, these advanced graph algorithms pop up occasionally:

**Dijkstra's Algorithm:**Finds the shortest path from one node to all other nodes in a*weighted*graph.**Topological Sort:**Arranges the nodes in a*directed*,*acyclic*graph in a special order based on incoming edges.**Minimum Spanning Tree:**Finds the cheapest set of edges needed to reach all nodes in a*weighted*graph.