A **Spanning Tree (ST)** of a connected undirected weighted graph **G** is a subgraph of **G** that is a **tree** and **connects (spans) all vertices of G**. A graph

**G**can have many STs (see

__this__or

__this__), each with different total weight (the sum of edge weights in the ST).

A **Min(imum) Spanning Tree (MST)** of **G** is an ST of **G** that has the **smallest total weight** among the various STs.

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The **MST** problem is a standard graph (and also optimization) problem defined as follows: Given a connected undirected weighted graph **G = (V, E)**, select a **subset** of edges of **G** such that the graph is still connected but with minimum total weight. The output is either the actual MST of **G** (there can be several possible MSTs of **G**) or usually just the minimum total weight itself (this is unique).

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Government wants to link **N** rural villages in the country with **N-1** roads.

(that is a *spanning tree* with **N** vertices and **N-1** edges).

The cost to build a road to connect two villages depends on the terrain, distance, etc.

(that is a *complete undirected weighted graph* of **N*(N-1)/2** weighted edges).

You want to minimize the total building cost. How are you going to build the roads?

(that is *minimum spanning tree*).

PS: There is a variant of this problem that requires more advanced solution, e.g., see __this__.

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The **MST** problem has polynomial solutions.

In this visualization, we will learn two of them: Kruskal's algorithm and Prim's algorithm. Both are classified as **Greedy** Algorithms. Note that there are other MST algorithms outside the two presented here.

Pro-tip 3: Other than using the typical media UI at the bottom of the page, you can also control the animation playback using keyboard shortcuts (in Exploration Mode): **Spacebar** to play/pause/replay the animation, **←**/**→** to step the animation backwards/forwards, respectively, and **-**/**+** to decrease/increase the animation speed, respectively.

View the visualisation of MST algorithm above.

Originally, all vertices and edges in the input graph are colored with the standard black color on white background.

At the end of the MST algorithm, **|V|-1** MST edges (and all **|V|** vertices) will be colored orange and non-MST edges will be colored grey.

There are two different sources for specifying an input graph:

**Edit Graph**: You can edit the currently displayed connected undirected weighted graph or draw your own input graph.**Example Graphs**: You can select from the list of example connected undirected weighted graphs to get you started.

**Kruskal's algorithm**: An O(**E** log **V**) greedy MST algorithm that grows a forest of minimum spanning trees and eventually combine them into one MST.

Kruskal's requires __a good sorting algorithm__ to sort edges of the input graph (usually stored in an __Edge List__ data structure) by non-decreasing weight and another data structure called __Union-Find Disjoint Sets (UFDS)__ to help in checking/preventing cycle.

Kruskal's algorithm first sort the set of edges **E** in non-decreasing weight (there can be edges with the same weight), and if ties, by increasing smaller vertex number of the edge, and if still ties, by increasing larger vertex number of the edge.

Discussion: Is this the only possible sort criteria?

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Then, Kruskal's algorithm will perform a loop through these sorted edges (that already have non-decreasing weight property) and **greedily** taking the next edge **e** if it does **not** create any cycle w.r.t. edges that have been taken earlier.

Without further ado, let's try

on the default example graph (that has three edges with the same weight). Go through this animated example first before continuing.To see on why the **Greedy Strategy** of Kruskal's algorithm works, we define a **loop invariant**: Every edge **e** that is added into tree **T** by Kruskal's algorithm is part of the **MST**.

At the start of Kruskal's main loop, **T = {}** is always part of **MST** by definition.

Kruskal's has a special cycle check in its main loop (using __UFDS__ data structure) and only add an edge **e** into **T** if it will never form a cycle w.r.t. the previously selected edges.

At the end of the main loop, Kruskal's can only select **V**-1 edges from a connected undirected weighted graph **G** without having any cycle. This implies that Kruskal's produces a Spanning Tree.

On the default example, notice that after taking the first 2 edges: 0-1 and 0-3, in that order, Kruskal's **cannot** take edge 1-3 as it will cause a cycle 0-1-3-0. Kruskal's then take edge 0-2 but it cannot take edge 2-3 as it will cause cycle 0-2-3-0.

We have seen in the previous slide that Kruskal's algorithm will produce a tree **T** that is a Spanning Tree (ST) when it stops. But is it the minimum ST, i.e., the **MST**?

To prove this, we need to recall that **before** running Kruskal's main loop, we have already sort the edges in non-decreasing weight, i.e., the latter edges will have equal or **larger** weight than the earlier edges.

At the start of every loop, **T** is always part of MST.

If Kruskal's only add a legal edge **e** (that will not cause cycle w.r.t. the edges that have been taken earlier) with **min cost**, then we can be sure that **w(T U e) ≤ w(T U any other unprocessed edge e' that does not form cycle)** (by virtue that Kruskal's has sorted the edges, so **w(e) ≤ w(e')**).

Therefore, at the end of the loop, the Spanning Tree **T** must have minimal overall weight **w(T)**, so **T** is the final MST.

On the default example, notice that after taking the first 2 edges: 0-1 and 0-3, in that order, and ignoring edge 1-3 as it will cause a cycle 0-1-3-0, we can safely take the next smallest legal edge 0-2 (with weight 2) as taking any other legal edge (e.g., edge 2-3 with **larger** weight 3) will either create **another** MST with equal weight (not in this example) or **another** ST that is not minimum (which is this example).

There are two parts of Kruskal's algorithm: Sorting and the Kruskal's main loop.

The sorting of edges is easy. We just store the graph using **Edge List** __data structure__ and sort **E** edges using any O(**E** log **E**) = O(**E** log **V**) __sorting algorithm__ (or just use C++/Python/Java sorting library routine) by non-decreasing weight, smaller vertex number, higher vertex number. This O(**E** log **V**) is the bottleneck part of Kruskal's algorithm as the second part is actually lighter, see below.

Kruskal's main loop can be easily implemented using __Union-Find Disjoint Sets__ data structure. We use **IsSameSet(u, v)** to test if taking edge **e** with endpoints **u** and **v** will cause a cycle (same connected component -- there is another path in the subtree that can connect **u** to **v**, thus adding edge **(u, v)** will cause a cycle) or not. If **IsSameSet(u, v)** returns false, we greedily take this next smallest and legal edge **e** and call **UnionSet(u, v)** to prevent future cycles involving this edge. This part runs in O(**E**) as we assume UFDS **IsSameSet(u, v)** and **UnionSet(u, v)** operations run in O(**1**) for a relatively small graph.

**Prim's algorithm**: Another O(**E** log **V**) greedy MST algorithm that grows a Minimum Spanning Tree from a starting source vertex until it spans the entire graph.

Prim's requires a Priority Queue data structure (usually implemented using __Binary Heap__ but we can also use __Balanced Binary Search Tree__ too) to dynamically order the currently considered edges based on non-decreasing weight, an __Adjacency List data structure__ for fast neighbor enumeration of a vertex, and a Boolean array (__a Direct Addressing Table__) to help in checking cycle.

Another name of Prim's algorithm is Jarnik-Prim's algorithm.

Prim's algorithm starts from a designated source vertex **s** (usually vertex 0) and enqueues all edges incident to **s** into a Priority Queue (PQ) according to non-decreasing weight, and if ties, by increasing vertex number (of the neighboring vertex number). Then it will repeatedly do the following greedy steps: If the vertex **v** of the front-most edge pair information **e: (w, v)** in the PQ has **not** been visited, it means that we can greedily extends the tree **T** to include vertex **v** and enqueue edges connected to **v** into the PQ, otherwise we discard edge **e** (because Prim's grows one spanning tree from **s**, the fact that **v** is already visited implies that there is another path from **s** to **v** and adding this edge will cause a cycle).

Without further ado, let's try **s = 1**. Go through this animated example first before continuing.

Prim's algorithm is a **Greedy Algorithm** because at each step of its main loop, it always try to select the next valid edge **e** with minimal weight (that is greedy!).

To convince us that Prim's algorithm is correct, let's go through the following simple proof: Let **T** be the spanning tree of graph **G** generated by Prim's algorithm and **T*** be the spanning tree of **G** that is known to have minimal cost, i.e. **T*** is the **MST**.

If **T == T***, that's it, Prim's algorithm produces exactly the same **MST** as **T***, we are done.

But if **T != T***...

Assume that on the default example, **T = {0-1, 0-3, 0-2}** but **T* = {0-1, 1-3, 0-2}** instead.

Let **e _{k} = (u, v)** be the first edge chosen by Prim's Algorithm at the

**k**-th iteration that is not in

**T***(on the default example,

**k = 2**,

**e**, note that

_{2}= (0, 3)**(0, 3)**is not in

**T***).

Let **P** be the path from **u** to **v** in **T***, and let **e*** be an edge in **P** such that one endpoint is in the tree generated at the (**k**−1)-th iteration of Prim's algorithm and the other is not (on the default example, **P = 0-1-3** and **e* = (1, 3)**, note that vertex **1** is inside **T** at first iteration **k = 1**).

If the weight of **e*** is less than the weight of **e _{k}**, then Prim's algorithm would have chosen

**e***on its

**k**-th iteration as that is how Prim's algorithm works.

So, it is certain that **w(e*) ≥ w(e _{k})**.

(on the example graph,

**e* = (1, 3)**has weight 1 and

**e**also has weight 1).

_{k}= (0, 3)When weight **e*** is = weight **e _{k}**, the choice between the

**e***or

**e**is actually arbitrary. And whether the weight of

_{k}**e***is ≥ weight of

**e**,

_{k}**e***can always be substituted with

**e**while preserving minimal total weight of

_{k}**T***. (on the example graph, when we replace

**e* = (1, 3)**with

**e**, we manage to transform

_{k}= (0, 3)**T***into

**T**).

But if **T != T***... (continued)

We can repeat the substitution process outlined earlier repeatedly until **T* = T** and thereby we have shown that the spanning tree generated by any instance of Prim's algorithm (from any source vertex **s**) is an MST as whatever the optimal MST is, it can be transformed to the output of Prim's algorithm.

We can easily implement Prim's algorithm with two well-known data structures:

- A Priority Queue PQ (
__Binary Heap__inside C++ STL priority_queue/Python heapq/Java PriorityQueue or__Balanced BST__inside C++ STL set/Java TreeSet), and - A Boolean array of size
**V**, essentially a__Direct Addressing Table__(to decide if a vertex has been taken or not, i.e., in the same connected component as the source vertex**s**or not).

With these, we can run Prim's Algorithm in O(**E** log **V**) because we process each edge once and each time, we call **Insert((w, v))** and **(w, v) = ExtractMax()** from a PQ in O(log **E**) = O(log **V ^{2}**) = O(2 log

**V**) = O(log

**V**). As there are

**E**edges, Prim's Algorithm runs in O(

**E**log

**V**).

Quiz: **Having seen both Kruskal's and Prim's Algorithms, which one is the better MST algorithm?**

Discussion: Why?

The content of this interesting slide (the answer of the usually intriguing discussion point from the earlier slide) is hidden and only available for legitimate CS lecturer worldwide. This mechanism is used in the various __flipped classrooms__ in NUS.

**If you are really a CS lecturer (or an IT teacher)** (outside of NUS) and are interested to know the answers, please drop an email to stevenhalim at gmail dot com (**show your University staff profile/relevant proof to Steven**) for Steven to manually activate this CS lecturer-only feature for you.

FAQ: This feature will **NOT** be given to anyone else who is not a CS lecturer.

You have reached the end of the basic stuffs of this Min(imum) Spanning Tree graph problem and its two classic algorithms: Kruskal's and Prim's (there are others, like another O(E log V) __Boruvka's__ algorithm, but not discussed in this visualization). We encourage you to explore further in the **Exploration Mode**.

However, the harder MST problems can be (much) more challenging that its basic version.

Once you have (roughly) mastered this MST topic, we encourage you to study more on harder graph problems where MST is used as a component, e.g., approximation algorithm for NP-hard __(Metric No-Repeat) TSP__ and __Steiner Tree__ problems.

We write a few MST problem variants in the __Competitive Programming book__.

- Max(imum) Spanning Tree,
- Min(imum) Spanning Subgraph,
- Min(imum) Spanning Forest,
- Second Best Spanning Tree,
- Minimax (Maximin) Path Problem, etc

Advertisement: Buy CP book to study more about these variants and see that sometimes Kruskal's is better and sometimes Prim's is better at some of these variants.

For a few more challenging questions about this MST problem and/or Kruskal's/Prim's Algorithms, please practice on __MST__ training module (no login is required, but on medium difficulty setting only).

However, for NUS students, you should login to officially clear this module and such achievement will be recorded in your user account.

This MST problem can be much more challenging than this basic form. Therefore we encourage you to try the following two ACM ICPC contest problems about MST: __UVa 01234 - RACING__ and __Kattis - arcticnetwork__.

Try them to consolidate and improve your understanding about this graph problem.

You are allowed to use/modify our implementation code for Kruskal's/Prim's Algorithms:__kruskal.cpp__ | __py__ | __java__ | __ml____prim.cpp__ | __py__ | __java__ | __ml__

The content of this interesting slide (the answer of the usually intriguing discussion point from the earlier slide) is hidden and only available for legitimate CS lecturer worldwide. This mechanism is used in the various __flipped classrooms__ in NUS.

**If you are really a CS lecturer (or an IT teacher)** (outside of NUS) and are interested to know the answers, please drop an email to stevenhalim at gmail dot com (**show your University staff profile/relevant proof to Steven**) for Steven to manually activate this CS lecturer-only feature for you.

FAQ: This feature will **NOT** be given to anyone else who is not a CS lecturer.

You have reached the last slide. Return to 'Exploration Mode' to start exploring!

Note that if you notice any bug in this visualization or if you want to request for a new visualization feature, do not hesitate to drop an email to the project leader: Dr Steven Halim via his email address: stevenhalim at gmail dot com.

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