Help Understanding Graph BFS code

[earnest inlet]

Hey guys Im a bit confused by this code for the BFS of a graph. This is chatgpt generated because I couldn't find a good example of a graph implemented with an adjacency matrix.
By the logic implemented here the visited vector is completely set to true by the second row of the graph. Is that supposed to be the case?

#include <iostream>
#include <queue>
#include <vector>

using namespace std;

// Function to perform BFS traversal
void BFS(vector<vector<int>>& graph, int start, vector<bool>& visited) {
    queue<int> q;
    q.push(start);
    visited[start] = true;

    while (!q.empty()) {
        int current = q.front();
        cout << current << " ";
        q.pop();

        // Visit all adjacent nodes of current node
        for (int i = 0; i < graph.size(); ++i) {
            if (graph[current][i] && !visited[i]) {
                q.push(i);
                visited[i] = true;
            }
        }
    }
}

int main() {
    // Example adjacency matrix representing a graph
    vector<vector<int>> graph = {
            {0, 1, 1, 0, 0},
            {1, 0, 0, 1, 1},
            {1, 0, 0, 0, 1},
            {0, 1, 0, 0, 0},
            {0, 1, 1, 0, 0}
    };

    // Number of vertices in the graph
    int numVertices = graph.size();

    // Initialize visited array to keep track of visited nodes
    vector<bool> visited(numVertices, false);



    cout << "BFS Traversal starting from node 0: ";
    BFS(graph, 0, visited);
    cout << endl;

    return 0;
}

[dawn bison]

Is that supposed to be the case?
Yep.
The root node_0 is connected with node_1 and node_2. We visit 3 nodes and put node_1 and node_2 in the queue.
Then we pop node_1 from the queue and visit nodes that connected to it: node_4 and node_5.
So by the second row we visited all nodes.

[earnest inlet]

lol its confusing me. I cant wrap my head around the representation i guess. What is the point of the 1's in the 3rd fourth and fifth rows? Any advice as to how to visualize this?

[dawn bison]

The matrix shows connections. If a value set to 1 than there is a path between nodes. In this particular matrix A[0][1] == 1, so there is a path from node_0 to node_1. Also A[1][0] == 1 -- there is a path in the opposite direction: from node_1 to node_0.

A graph may have one directional paths, like A[0][1] == 1 and A[1][0] == 0, than we can only go from node_0 to node_1

[earnest inlet]

ahhh ok yes I see how that input was formed now.