5.5 KiB
Silver - DFS
Author: Siyong Huang
Overview
- Prerequisites
- Depth First Search (DFS)
- Flood Fill
- Graph Two-Coloring
- Cycle Detection
Prerequisites
- CSAcademy Graph Theory
- CSAcademy Graph Representations
- Note: DFS is most commonly implemented with adjacency lists
Depth First Search (DFS)
Depth First Search, more commonly DFS, is a fundamental graph algorithm that traverses an entire connected component. The rest of this document describes various applications of DFS. Of course, it is one possible way to implement flood fill.
Tutorial
- Recommended:
- Additional:
- CPH Chapter 12
- cp-algo DFS
- hard to parse if this is your first time learning about DFS
Problems
- Mootube, Silver (Easy)
- Closing the Barn, Silver (Easy)
- Moocast, Silver (Easy)
- Pails (Normal)
- Milk Visits (Normal)
Flood Fill
Flood Fill refers to finding the number of connected components in a graph, usually when the graph is a grid.
Tutorial
- Recommended:
Problems
- Ice Perimeter (Easy)
- Switching on the Lights (Moderate)
- Build Gates (Moderate)
- Why Did the Cow Cross the Road III, Silver (Moderate)
- Multiplayer Moo (Hard)
Graph Two-Coloring
Graph two-colorings is assigning a boolean value to each node of the graph, dictated by the edge configuration The most common example of a two-colored graph is a bipartite graph, in which each edge connects two nodes of opposite colors
Tutorial
The idea is that we can arbitrarily label a node and then run DFS. Every time we visit a new (unvisited) node, we set its color based on the edge rule. When we visit a previously visited node, check to see whether its color matches the edge rule. For example, an implementation of coloring a bipartite graph is shown below.
bool is_bipartite = true;
void dfs(int node)
{
visited[node] = true;
for(int u:adj_list[node])
if(visited[u])
{
if(color[u] == color[node])
is_bipartite = false;
}
else
{
color[u] = !color[node];
dfs(u);
}
}
- Additional:
- Bipartite Graphs: cp-alg bipartite check
- Note: CP Algorithm uses BFS, but DFS accomplishes the same task
- Bipartite Graphs: cp-alg bipartite check
Problems
Cycle Detection
A cycle is a non-empty path of distinct edges that start and end at the same node. Cycle detection determines properties of cycles in a directed or undirected graph, such as whether each node of the graph is part of a cycle.
Functional Graphs
Links:
- CPH 16.3: successor paths
- CPH 16.4: cycle detection in successor graph
In silver-level directed cycle problems, it is generally the case that each node has exactly one edge going out of it. This is known as a successor graph or a functional graph.
The following sample code counts the number of cycles in such a graph. The "stack" contains nodes that can reach the current node. If the current node points to a node v
on the stack (on_stack[v]
is true), then we know that a cycle has been created. However, if the current node points to a node v
that has been previously visited but is not on the stack, then we know that the current chain of nodes points into a cycle that has already been considered.
(test code?)
//Each node points to next_node[node]
bool visited[MAXN], on_stack[MAXN];
int number_of_cycles = 0;
void dfs(int n)
{
visited[n] = on_stack[n] = true;
int u = next_node[n];
if(on_stack[u])
number_of_cycles++;
else if(!visited[u])
dfs(u);
on_stack[n] = false;
}
int main()
{
//read input, etc
for(int i = 1;i <= N;i++)
if(!visited[i])
dfs(i);
}
For non-functional directed graphs, this code will still detect a cycle if it exists (though number_of_cycles
will be meaningless).
Problems
- Codeforces 1020B. Badge (Very Easy)
- Try to solve the problem in O(N)!
- The Bovine Shuffle (Normal)
- Swapity Swapity Swap (Very Hard)
- CSES Round Trip (undirected)
- CSES Round Trip II (directed)
Cycle finding is also related to strongly connected components, a platinum level concept.