圖論演算法

# For edges list
graph = defaultdict(list)
for u, v in edges:
    graph[u].append(v)
    graph[v].append(u)  # Undirected

# For weighted edges
graph = defaultdict(list)
for u, v, w in edges:
    graph[u].append((v, w))

# For unweighted
graph = [[0] * n for _ in range(n)]
for u, v in edges:
    graph[u][v] = 1
    graph[v][u] = 1  # Undirected

# For weighted
graph = [[float('inf')] * n for _ in range(n)]
for u, v, w in edges:
    graph[u][v] = w

# 4-directional movement
directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]

# 8-directional movement
directions = [(0, 1), (1, 0), (0, -1), (-1, 0),
              (1, 1), (1, -1), (-1, 1), (-1, -1)]

# Check bounds
def is_valid(r, c, rows, cols):
    return 0 <= r < rows and 0 <= c < cols

# undirected: add BOTH directions -- forgetting this is the most common graph bug
for u, v in edges:
    graph[u].append(v)
    graph[v].append(u)

# directed: one direction only, and degree splits into two counts
out_deg = [0] * n
in_deg = [0] * n
for u, v in edges:
    graph[u].append(v)
    out_deg[u] += 1
    in_deg[v] += 1

def graph_algorithm(n, edges):
    # Build adjacency list
    graph = collections.defaultdict(list)
    for u, v in edges:
        graph[u].append(v)
        graph[v].append(u)  # For undirected
    
    # Track visited nodes
    visited = set()
    
    # Process each component
    result = 0
    for node in range(n):
        if node not in visited:
            # Process component
            process_component(node, graph, visited)
            result += 1
    
    return result

# Set for simple visited
visited = set()

# Array for state tracking
# 0: unvisited, 1: visiting, 2: visited
state = [0] * n

# Dictionary for path reconstruction
parent = {}

def bfs_template(graph, start):
    """Breadth-first search template"""
    from collections import deque
    
    visited = set([start])
    queue = deque([start])
    level = 0
    
    while queue:
        # Process level by level
        size = len(queue)
        for _ in range(size):
            node = queue.popleft()
            
            # Process node
            for neighbor in graph[node]:
                if neighbor not in visited:
                    visited.add(neighbor)
                    queue.append(neighbor)
        level += 1
    
    return level

def dfs_template(graph, start):
    """Depth-first search template"""
    visited = set()
    path = []
    
    def dfs(node):
        visited.add(node)
        path.append(node)
        
        for neighbor in graph[node]:
            if neighbor not in visited:
                dfs(neighbor)
        
        # Backtrack if needed
        # path.pop()
    
    dfs(start)
    return visited

// java
// LC 797 - All Paths From Source to Target
// IDEA: DFS + backtracking on a DAG. No visited set (DAG => no cycles),
//       and a node may legitimately appear in many different paths.
// time = O(2^n * n), space = O(n) excluding output
import java.util.*;

public class Solution {
    public List<List<Integer>> allPathsSourceTarget(int[][] graph) {
        List<List<Integer>> res = new ArrayList<>();
        List<Integer> path = new ArrayList<>();
        path.add(0);                       // start node is always in the path
        dfs(graph, 0, path, res);
        return res;
    }

    private void dfs(int[][] graph, int u, List<Integer> path, List<List<Integer>> res) {
        if (u == graph.length - 1) {
            res.add(new ArrayList<>(path));   // NOTE: copy, not the live list
            return;
        }
        for (int v : graph[u]) {
            path.add(v);
            dfs(graph, v, path, res);
            path.remove(path.size() - 1);     // backtrack
        }
    }
}

# python
# LC 797 - All Paths From Source to Target
# IDEA: DFS + backtracking on a DAG (no visited set needed)
# time = O(2^n * n), space = O(n) excluding output
class Solution(object):
    def allPathsSourceTarget(self, graph):
        n = len(graph)
        res, path = [], [0]

        def dfs(u):
            if u == n - 1:
                res.append(path[:])    # copy!
                return
            for v in graph[u]:
                path.append(v)
                dfs(v)
                path.pop()             # backtrack

        dfs(0)
        return res

# [[1,2],[3],[3],[]] -> [[0,1,3],[0,2,3]]

class UnionFind:
    def __init__(self, n):
        self.parent = list(range(n))
        self.rank = [0] * n
        self.count = n
    
    def find(self, x):
        """Find with path compression"""
        if self.parent[x] != x:
            self.parent[x] = self.find(self.parent[x])
        return self.parent[x]
    
    def union(self, x, y):
        """Union by rank"""
        px, py = self.find(x), self.find(y)
        if px == py:
            return False
        
        if self.rank[px] < self.rank[py]:
            px, py = py, px
        self.parent[py] = px
        if self.rank[px] == self.rank[py]:
            self.rank[px] += 1
        
        self.count -= 1
        return True
    
    def connected(self, x, y):
        return self.find(x) == self.find(y)

def topological_sort_bfs(n, edges):
    """Kahn's algorithm for topological sort"""
    from collections import deque
    
    graph = collections.defaultdict(list)
    indegree = [0] * n
    
    for u, v in edges:
        graph[u].append(v)
        indegree[v] += 1
    
    queue = deque([i for i in range(n) if indegree[i] == 0])
    result = []
    
    while queue:
        node = queue.popleft()
        result.append(node)
        
        for neighbor in graph[node]:
            indegree[neighbor] -= 1
            if indegree[neighbor] == 0:
                queue.append(neighbor)
    
    return result if len(result) == n else []

# Directed graph - DFS with states
def has_cycle_directed(graph):
    # 0: unvisited, 1: visiting, 2: visited
    state = [0] * n
    
    def dfs(node):
        if state[node] == 1:  # Back edge
            return True
        if state[node] == 2:
            return False
        
        state[node] = 1
        for neighbor in graph[node]:
            if dfs(neighbor):
                return True
        state[node] = 2
        return False

# Undirected graph - Union-Find
def has_cycle_undirected(edges):
    uf = UnionFind(n)
    for u, v in edges:
        if not uf.union(u, v):
            return True  # Already connected
    return False

def is_bipartite_bfs(graph):
    """Check if graph is bipartite using BFS"""
    from collections import deque

    n = len(graph)
    colors = [-1] * n  # -1: uncolored, 0: color A, 1: color B

    # Handle disconnected components
    for start in range(n):
        if colors[start] != -1:
            continue

        # BFS coloring
        queue = deque([start])
        colors[start] = 0

        while queue:
            node = queue.popleft()

            for neighbor in graph[node]:
                if colors[neighbor] == -1:
                    # Color with opposite color
                    colors[neighbor] = 1 - colors[node]
                    queue.append(neighbor)
                elif colors[neighbor] == colors[node]:
                    # Same color conflict - not bipartite
                    return False

    return True

def is_bipartite_dfs(graph):
    """Check if graph is bipartite using DFS"""
    n = len(graph)
    colors = [-1] * n

    def dfs(node, color):
        colors[node] = color

        for neighbor in graph[node]:
            if colors[neighbor] == -1:
                # Recursively color with opposite color
                if not dfs(neighbor, 1 - color):
                    return False
            elif colors[neighbor] == colors[node]:
                # Same color conflict
                return False

        return True

    # Check each component
    for i in range(n):
        if colors[i] == -1:
            if not dfs(i, 0):
                return False

    return True

Graph Algorithm Selection Flowchart:

1. What is the problem asking for?
   ├── Find shortest path → Continue to 2
   ├── Check connectivity → Use Union-Find or DFS/BFS
   ├── Order with dependencies → Use Topological Sort
   ├── Detect cycles → Use DFS with states or Union-Find
   └── Traverse all nodes → Use BFS or DFS

2. For shortest path problems:
   ├── Unweighted graph → Use BFS
   ├── Non-negative weights → Use Dijkstra
   ├── Negative weights → Use Bellman-Ford
   └── All pairs → Use Floyd-Warshall

3. For connectivity problems:
   ├── Static graph → Use DFS/BFS once
   ├── Dynamic connections → Use Union-Find
   └── Count components → Use either approach

4. For traversal problems:
   ├── Level-by-level → Use BFS
   ├── Path finding → Use DFS with backtracking
   └── State space search → Use BFS for optimal

5. Is the graph special?
   ├── Tree → Simpler DFS/BFS
   ├── DAG → Topological sort possible
   ├── Bipartite → Two-coloring
   └── Grid → Treat as implicit graph