### Dynamic Graphs: Minimum Spanning Tree (MST)

Chapter 5: Minimum Spanning Tree in Dynamic Graphs

# Chapter 5: Minimum Spanning Tree (MST) in Dynamic Graphs

## Introduction

The Minimum Spanning Tree is a tree that spans all the vertices in a connected, undirected graph and has the minimum possible total edge weight. While classic algorithms like Kruskal's and Prim's are effective for static graphs, dynamic graphs pose additional complexities. This chapter will delve into approaches for handling MSTs in dynamic scenarios.

## Classic Algorithms for MST

Prim's and Kruskal's algorithms are often used to find the MST in a static graph. Here's a quick outline of Prim's algorithm:

``````
PrimMST(Graph G, Node s):
1. Initialize a priority queue Q with vertex s having distance 0 and all other vertices with distance Infinity
2. Initialize an empty set M to store the MST edges
3. While Q is not empty:
4. Extract vertex u with minimum distance from Q
5. For each edge (u, v) in G:
6. If v is in Q and weight(u, v) < distance[v]:
7. Update distance[v] in Q
8. Add edge (u, v) to M
``````

## Dynamic Graphs and MST

The primary challenges for maintaining MSTs in dynamic graphs are:

• When edges are added or updated, the MST might require adjustments.
• When edges are removed, the existing MST may no longer be valid and may require reevaluation.

## MST in Dynamic Graphs

Strategies to adapt MST algorithms for dynamic graphs include:

1. Recomputing from Scratch: Simple but can be computationally intensive.
2. Incremental Updates: Update the MST incrementally based on the changes.

### Incremental MST Algorithm

``````
IncrementalMST(Graph G_t, MST_Tree):
2. Update MST_Tree according to the changes
3. Correct any inconsistencies in MST_Tree
``````

## Conclusion

Maintaining a Minimum Spanning Tree in dynamic graphs is a challenging yet feasible task. By leveraging incremental algorithms, one can efficiently adapt to changes in the graph while keeping the computational cost relatively low.