Introduction to Functional Graphs
Authors: Siyong Huang, Benjamin Qi, Andrew Wang
Contributors: Chuyang Wang, Ryan Chou
Directed graphs in which every vertex has exactly one outgoing edge.
Introduction
In a functional graph, each node has exactly one out-edge. This is also commonly referred to as a successor graph.
Resources | ||||
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CPH | diagrams |
You can think of every connected component of a functional graph as a rooted tree with all edges directed toward the root plus an additional edge going out of the root.
Floyd's Algorithm
Floyd's Algorithm, also commonly referred to as the Tortoise and Hare Algorithm, is capable of detecting cycles in a functional graph in time and memory (not counting the graph itself).
Example - Cooperative Game
Focus Problem – try your best to solve this problem before continuing!
Hint 1
Hint 2
Solution
Using Floyd's Algorithm, we can find some node on the cycle after queries. Then we can find the first node in the cycle after another queries.
C++
#include <iostream>#include <string>#include <vector>using std::cout;using std::endl;using std::pair;using std::vector;vector<int> move_result(const vector<int> &to_move) {
Java
import java.io.*;import java.util.*;public class CoopGame {static BufferedReader read =new BufferedReader(new InputStreamReader(System.in));public static void main(String[] args) throws IOException {moveResult(new int[] {0, 1});int[] groups = moveResult(new int[] {1});
Python
def move_result(to_move):"""Type annotations break the grader for some reason,so here's the signature of the function:move_result(to_move: Iterable[int]) -> list[int]"""print(f"next {' '.join(str(i) for i in to_move)}", flush=True)res = input().split()groups = [0 for _ in range(10)]
Do you see why this is equivalent to the code mentioned in CPH?
Python
a = succ(x)b = succ(succ(x))while a != b:a = succ(a)b = succ(succ(b))
Java
a = succ(x);b = succ(succ(x));while (a != b) {a = succ(a);b = succ(succ(b));}
C++
a = succ(x);b = succ(succ(x));while (a != b) {a = succ(a);b = succ(succ(b));}
corresponds to friend and corresponds to friend .
Python
a = xwhile a != b:a = succ(a)b = succ(b)first = a
C++
a = x;while (a != b) {a = succ(a);b = succ(b);}first = a;
Java
a = x;while (a != b) {a = succ(a);b = succ(b);}first = a;
corresponds to friends and corresponds to friends and .
Example - Badge
Focus Problem – try your best to solve this problem before continuing!
While the constraints allow for a solution, it's possible to do this in just !
Solution 1
C++
#include <iostream>#include <vector>using std::cout;using std::endl;using std::vector;vector<int> p;vector<int> ans;bool in_cycle = false;
Java
import java.io.*;import java.util.*;public class Badge {public static int[] p;public static int[] ans;public static boolean in_cycle;public static void dfs(int n) {if (ans[n] != -2) {
Python
import syssys.setrecursionlimit(10**5)n = int(input())p = [int(i) - 1 for i in input().split()]assert n == len(p)def dfs(n: int) -> None:
This code generates the answer independently for each connected component. Note that it uses 0-indexing, not 1-indexing.
Try simulating the algorithm on the following directed graph in CSAcademy's Graph Editor.
0 1 1 2 2 3 3 4 4 2 5 6 6 1
On the first step, we make the following recursive calls:
dfs(0)
->dfs(1)
->dfs(2)
->dfs(3)
->dfs(4)
->dfs(2)
, at which point we stop sinceans[2] = -1
. Since we have reached2
for the second time, we know that2
is part of a cycle andans[2] = 2
. Similarly,ans[3] = 3
andans[4] = 4
since they are part of the cycle. On the other hand,ans[0] = ans[1] = 2
since neither of them are part of the cycle.Later, we make the following recursive calls when we start at vertex
5
:dfs(5)
->dfs(6)
->dfs(1)
. We already know thatans[1] = 2
, soans[5] = ans[6] = 2
as well.
Solution 2
floyd(x)
generates answers for all vertices in the connected component containing
x
.
Note that this requires reverse adjacency lists.
In the code, these are stored in the variable radj
.
C++
#include <bits/stdc++.h>using namespace std;int n;vector<int> adj, ans;vector<vector<int>> radj;void fill_radj(int x) {for (auto &child : radj[x]) {/*
Java
import java.io.*;import java.util.*;public class Badge {static int[] adj;static int[] ans;/** For each node, we need a list to store its children; at nodes* combining the cycle with other part of the connected component, there* would be more than one outgoing arrow in the reversed adjacency list
Python
n = int(input())adj = [i - 1 for i in list(map(int, input().split()))]radj = [[] for _ in range(n)]ans = [-1] * ndef fill_radj(x: int) -> None:global ans"""
It's also possible to use floyd(x)
to generate answers for all vertices in the connected component
containing x
without using adjacency lists.
Java
import java.io.*;import java.util.*;public class Badge {static int[] ans;static int[] arr;public static void main(String[] args) throws IOException {BufferedReader x = new BufferedReader(new InputStreamReader(System.in));StringTokenizer st = new StringTokenizer(x.readLine());int n = Integer.parseInt(st.nextToken());
Count Cycles
The following 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.
Warning!
The code below is untested. Use it at your own risk.
C++
bool visited[MAXN];bool on_stack[MAXN];int number_of_cycles = 0;int next_node[MAXN];void dfs(int n) {visited[n] = on_stack[n] = true;int u = next_node[n];if (on_stack[u]) {number_of_cycles++;
Java
import java.io.*;import java.util.*;public class CountCycles {static boolean[] visited = new boolean[MAXN];static boolean[] onStack = new boolean[MAXN];static int numberOfCycles = 0;static int[] nextNode = new int[MAXN];public static void main(String[] args) throws IOException {
Python
vis = [False] * MAXNon_stack = [False] * MAXNnext_node = [0] * MAXNnumber_of_cycles = 0def dfs(n: int) -> None:global number_of_cyclesvis[n] = on_stack[n] = True
-th Successor
As described briefly in CPH 16.3, the -th successor of a certain node in a functional graph can be found in time using binary jumping, given time of preprocessing where is the maximum length of each jump. See the Platinum module for details.
Problems
Status | Source | Problem Name | Difficulty | Tags | |
---|---|---|---|---|---|
Silver | Easy | Show TagsFunctional Graph | |||
CSES | Easy | Show TagsFunctional Graph | |||
Silver | Normal | Show TagsSCC | |||
Old Silver | Normal | Show TagsFunctional Graph |
Quiz
What's a functional graph (successor graph)?
Additional problems involving functional graphs can be found in the Tree DP and Binary Jumping modules.
Module Progress:
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