LeetCode #913 — HARD

Cat and Mouse

Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.

Solve on LeetCode
The Problem

Problem Statement

A game on an undirected graph is played by two players, Mouse and Cat, who alternate turns.

The graph is given as follows: graph[a] is a list of all nodes b such that ab is an edge of the graph.

The mouse starts at node 1 and goes first, the cat starts at node 2 and goes second, and there is a hole at node 0.

During each player's turn, they must travel along one edge of the graph that meets where they are.  For example, if the Mouse is at node 1, it must travel to any node in graph[1].

Additionally, it is not allowed for the Cat to travel to the Hole (node 0).

Then, the game can end in three ways:

  • If ever the Cat occupies the same node as the Mouse, the Cat wins.
  • If ever the Mouse reaches the Hole, the Mouse wins.
  • If ever a position is repeated (i.e., the players are in the same position as a previous turn, and it is the same player's turn to move), the game is a draw.

Given a graph, and assuming both players play optimally, return

  • 1 if the mouse wins the game,
  • 2 if the cat wins the game, or
  • 0 if the game is a draw.

Example 1:

Input: graph = [[2,5],[3],[0,4,5],[1,4,5],[2,3],[0,2,3]]
Output: 0

Example 2:

Input: graph = [[1,3],[0],[3],[0,2]]
Output: 1

Constraints:

  • 3 <= graph.length <= 50
  • 1 <= graph[i].length < graph.length
  • 0 <= graph[i][j] < graph.length
  • graph[i][j] != i
  • graph[i] is unique.
  • The mouse and the cat can always move. 
Patterns Used

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: A game on an undirected graph is played by two players, Mouse and Cat, who alternate turns. The graph is given as follows: graph[a] is a list of all nodes b such that ab is an edge of the graph. The mouse starts at node 1 and goes first, the cat starts at node 2 and goes second, and there is a hole at node 0. During each player's turn, they must travel along one edge of the graph that meets where they are. For example, if the Mouse is at node 1, it must travel to any node in graph[1]. Additionally, it is not allowed for the Cat to travel to the Hole (node 0). Then, the game can end in three ways: If ever the Cat occupies the same node as the Mouse, the Cat wins. If ever the Mouse reaches the Hole, the Mouse wins. If ever a position is repeated (i.e., the players are in the same position as a previous turn, and it is the same player's turn to move), the game is a draw. Given a graph, and

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Math · Dynamic Programming · Topological Sort

Example 1

[[2,5],[3],[0,4,5],[1,4,5],[2,3],[0,2,3]]

Example 2

[[1,3],[0],[3],[0,2]]

Related Problems

  • Cat and Mouse II (cat-and-mouse-ii)
Step 02

Core Insight

What unlocks the optimal approach

  • No official hints in dataset. Start from constraints and look for a monotonic or reusable state.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. Continue until all input is consumed.
Use the first example testcase as your mental trace to verify each transition.
Step 04

Edge Cases

Minimum Input
Single element / shortest valid input
Validate boundary behavior before entering the main loop or recursion.
Duplicates & Repeats
Repeated values / repeated states
Decide whether duplicates should be merged, skipped, or counted explicitly.
Extreme Constraints
Largest constraint values
Re-check complexity target against constraints to avoid time-limit issues.
Invalid / Corner Shape
Empty collections, zeros, or disconnected structures
Handle special-case structure before the core algorithm path.
Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #913: Cat and Mouse
class Solution {
    private int n;
    private int[][] g;
    private int[][][] ans;
    private int[][][] degree;

    private static final int HOLE = 0, MOUSE_START = 1, CAT_START = 2;
    private static final int MOUSE_TURN = 0, CAT_TURN = 1;
    private static final int MOUSE_WIN = 1, CAT_WIN = 2, TIE = 0;

    public int catMouseGame(int[][] graph) {
        n = graph.length;
        g = graph;
        ans = new int[n][n][2];
        degree = new int[n][n][2];
        for (int i = 0; i < n; ++i) {
            for (int j = 1; j < n; ++j) {
                degree[i][j][MOUSE_TURN] = g[i].length;
                degree[i][j][CAT_TURN] = g[j].length;
            }
        }
        for (int i = 0; i < n; ++i) {
            for (int j : g[HOLE]) {
                --degree[i][j][CAT_TURN];
            }
        }
        Deque<int[]> q = new ArrayDeque<>();
        for (int j = 1; j < n; ++j) {
            ans[0][j][MOUSE_TURN] = MOUSE_WIN;
            ans[0][j][CAT_TURN] = MOUSE_WIN;
            q.offer(new int[] {0, j, MOUSE_TURN});
            q.offer(new int[] {0, j, CAT_TURN});
        }
        for (int i = 1; i < n; ++i) {
            ans[i][i][MOUSE_TURN] = CAT_WIN;
            ans[i][i][CAT_TURN] = CAT_WIN;
            q.offer(new int[] {i, i, MOUSE_TURN});
            q.offer(new int[] {i, i, CAT_TURN});
        }
        while (!q.isEmpty()) {
            int[] state = q.poll();
            int t = ans[state[0]][state[1]][state[2]];
            List<int[]> prevStates = getPrevStates(state);
            for (var prevState : prevStates) {
                int pm = prevState[0], pc = prevState[1], pt = prevState[2];
                if (ans[pm][pc][pt] == TIE) {
                    boolean win
                        = (t == MOUSE_WIN && pt == MOUSE_TURN) || (t == CAT_WIN && pt == CAT_TURN);
                    if (win) {
                        ans[pm][pc][pt] = t;
                        q.offer(prevState);
                    } else {
                        if (--degree[pm][pc][pt] == 0) {
                            ans[pm][pc][pt] = t;
                            q.offer(prevState);
                        }
                    }
                }
            }
        }
        return ans[MOUSE_START][CAT_START][MOUSE_TURN];
    }

    private List<int[]> getPrevStates(int[] state) {
        List<int[]> pre = new ArrayList<>();
        int m = state[0], c = state[1], t = state[2];
        int pt = t ^ 1;
        if (pt == CAT_TURN) {
            for (int pc : g[c]) {
                if (pc != HOLE) {
                    pre.add(new int[] {m, pc, pt});
                }
            }
        } else {
            for (int pm : g[m]) {
                pre.add(new int[] {pm, c, pt});
            }
        }
        return pre;
    }
}
Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(n^3)
Space
O(n^2)

Approach Breakdown

RECURSIVE
O(2ⁿ) time
O(n) space

Pure recursion explores every possible choice at each step. With two choices per state (take or skip), the decision tree has 2ⁿ leaves. The recursion stack uses O(n) space. Many subproblems are recomputed exponentially many times.

DYNAMIC PROGRAMMING
O(n × m) time
O(n × m) space

Each cell in the DP table is computed exactly once from previously solved subproblems. The table dimensions determine both time and space. Look for the state variables — each unique combination of state values is one cell. Often a rolling array can reduce space by one dimension.

Shortcut: Count your DP state dimensions → that’s your time. Can you drop one? That’s your space optimization.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Overflow in intermediate arithmetic

Wrong move: Temporary multiplications exceed integer bounds.

Usually fails on: Large inputs wrap around unexpectedly.

Fix: Use wider types, modular arithmetic, or rearranged operations.

State misses one required dimension

Wrong move: An incomplete state merges distinct subproblems and caches incorrect answers.

Usually fails on: Correctness breaks on cases that differ only in hidden state.

Fix: Define state so each unique subproblem maps to one DP cell.