# Discover the Largest Complete Subgraph

## Introduction: Discover the Largest Complete Subgraph

In graph theory, a complete subgraph is a subgraph in which every pair of distinct vertices is connected by a unique edge. Discovering the largest complete subgraph in a given graph is an important problem in many real-world applications, such as network analysis, community detection, and data mining.

In this lesson, we will explore the concept of the largest complete subgraph, discuss some real-world examples and scenarios where this problem arises, and walk through a step-by-step solution using actual code.

## Real-World Examples and Scenarios

Some real-world examples of the largest complete subgraph problem include:

**Social Network Analysis**: In a social network, a complete subgraph represents a clique, or a group of people who are all friends with each other. Identifying the largest clique can help detect communities or uncover influential groups within a network.

**Bioinformatics**: In protein-protein interaction networks, a complete subgraph may represent a group of proteins that interact with each other, potentially forming a functional complex. Identifying the largest complete subgraph can provide insights into the functional organization of proteins in a cell.

**Computer Vision**: In image processing, a complete subgraph can be used to represent a group of similar features or points in an image. Discovering the largest complete subgraph can help identify the most prominent structures or patterns within an image.

## Real-World Scenario: Social Network Analysis

Suppose we are given a social network represented as an undirected graph, where nodes represent people and edges represent friendships. Our goal is to find the largest group of people who are all friends with each other, i.e., the largest complete subgraph.

## Problem Statement

Given an undirected graph G = (V, E), where V is the set of vertices and E is the set of edges, find the largest complete subgraph.

## Problem Scenario and Statement

In our social network analysis scenario, the problem statement can be rephrased as: given a social network represented as an undirected graph, find the largest group of people who are all friends with each other.

## Solution: Bron-Kerbosch Algorithm

One popular approach to solve the largest complete subgraph problem is the Bron-Kerbosch algorithm. The Bron-Kerbosch algorithm is a backtracking algorithm that finds all cliques in an undirected graph. By finding all cliques, we can then identify the largest complete subgraph.

Here's a high-level outline of the Bron-Kerbosch algorithm:

- Start with an empty set of vertices R, and sets P and X containing all vertices.
- If both P and X are empty, R is a maximal clique, so process it and return.
- Choose a pivot vertex u from P or X.
- For each vertex v in P - N(u), where N(u) is the set of neighbors of u: a. Add v to R. b. Recursively call the algorithm with R + {v}, P ∩ N(v), and X ∩ N(v). c. Remove v from R and P, and add v to X.
- Return all cliques found.

## Code Solution

Let's implement the Bron-Kerbosch algorithm in Python:

```
def bron_kerbosch(graph, r=set(), p=None, x=set()):
if p is None:
p = set(graph.keys())
if not p and not x:
yield r
else:
u = next(iter(p | x)) # Choose a pivot vertex
for v in p - graph[u]:
yield from bron_kerbosch(graph, r | {v}, p & graph[v], x & graph[v])
p.remove(v)
x.add(v)
def find_largest_complete_subgraph(graph):
cliques = list(bron_kerbosch(graph))
return max(cliques, key=len)
```

## Calling Functions with Real-World Scenario Values

Let's use the following social network as an example:

```
A -- B -- C
| \ | |
D -- E -- F
```

We can represent this network as a dictionary where the keys are the nodes and the values are sets of neighboring nodes:

```
social_network = {
"A": {"B", "D", "E"},
"B": {"A", "C", "E"},
"C": {"B", "F"},
"D": {"A", "E"},
"E": {"A", "B", "D"},
"F": {"C"},
}
```

Now, let's find the largest complete subgraph:

```
largest_complete_subgraph = find_largest_complete_subgraph(social_network)
print(largest_complete_subgraph) # Output: {'A', 'D', 'E'}
```

## Explaining the Code Solution

In our implementation, `bron_kerbosch`

is a recursive function that finds all the cliques in a given graph. It takes the current graph, a set of vertices R, a set of candidate vertices P, and a set of excluded vertices X as input. The function iterates through the vertices in P, calls itself recursively with updated R, P, and X sets, and yields cliques when both P and X are empty.

The `find_largest_complete_subgraph`

function calls `bron_kerbosch`

and returns the largest clique in the graph by comparing their lengths.

## Solving Other Real-World Problems

The largest complete subgraph problem and the Bron-Kerbosch algorithm can be applied to other real-world scenarios mentioned earlier, such as bioinformatics and computer vision. By adjusting the input graph to represent relevant relationships (e.g., protein-protein interactions or image features), we can use the same algorithm to find the largest complete subgraph and gain insights into the underlying structures or patterns in these domains.