Adding Kosaraju's Algorithm to find strongly connected components

graph/kosaraju.go

@@ -0,0 +1,92 @@
+// kosaraju.go
+// description: Implementation of Kosaraju's algorithm to find Strongly Connected Components (SCCs) in a directed graph.
+// details: The algorithm consists of three steps:
+// 1. Perform DFS and fill the stack with vertices in the order of their finish times.
+// 2. Create a transposed graph by reversing all edges.
+// 3. Perform DFS on the transposed graph in the order defined by the stack to find SCCs.
+// time: O(V + E), where V is the number of vertices and E is the number of edges in the graph.
+// space: O(V), where V is the number of vertices in the graph.
+// ref link: https://en.wikipedia.org/wiki/Kosaraju%27s_algorithm
+// author: mapcrafter2048
+
+package graph
+
+// Kosaraju returns a list of Strongly Connected Components (SCCs).
+func (g *Graph) Kosaraju() [][]int {
+	stack := []int{}
+	visited := make([]bool, g.vertices)
+
+	// Step 1: Perform DFS and fill stack based on finish times.
+	for i := 0; i < g.vertices; i++ {
+		if !visited[i] {
+			g.fillOrder(i, visited, &stack)
+		}
+	}
+
+	// Step 2: Create a transposed graph.
+	transposed := g.transpose()
+
+	// Step 3: Perform DFS on the transposed graph in the order defined by the stack.
+	visited = make([]bool, g.vertices)
+	var sccs [][]int
+
+	for len(stack) > 0 {
+		// Pop vertex from stack
+		v := stack[len(stack)-1]
+		stack = stack[:len(stack)-1]
+
+		// Perform DFS if not already visited.
+		if !visited[v] {
+			scc := []int{}
+			transposed.dfs(v, visited, &scc)
+			sccs = append(sccs, scc)
+		}
+	}
+
+	return sccs
+}
+
+// Helper function to fill the stack with vertices in the order of their finish times.
+func (g *Graph) fillOrder(v int, visited []bool, stack *[]int) {
+	visited[v] = true
+
+	for neighbor := range g.edges[v] {
+		if !visited[neighbor] {
+			g.fillOrder(neighbor, visited, stack)
+		}
+	}
+
+	// Push the current vertex to the stack after exploring all neighbors.
+	*stack = append(*stack, v)
+}
+
+// Helper function to create a transposed (reversed) graph.
+func (g *Graph) transpose() *Graph {
+	transposed := &Graph{
+		vertices: g.vertices,
+		edges:    make(map[int]map[int]int),
+	}
+
+	for v, neighbors := range g.edges {
+		for neighbor := range neighbors {
+			if transposed.edges[neighbor] == nil {
+				transposed.edges[neighbor] = make(map[int]int)
+			}
+			transposed.edges[neighbor][v] = 1 // Add the reversed edge
+		}
+	}
+
+	return transposed
+}
+
+// Helper DFS function used in the transposed graph to collect SCCs.
+func (g *Graph) dfs(v int, visited []bool, scc *[]int) {
+	visited[v] = true
+	*scc = append(*scc, v)
+
+	for neighbor := range g.edges[v] {
+		if !visited[neighbor] {
+			g.dfs(neighbor, visited, scc)
+		}
+	}
+}

graph/kosaraju_test.go

@@ -0,0 +1,106 @@
+package graph
+
+import (
+	"reflect"
+	"sort"
+	"testing"
+)
+
+func TestKosaraju(t *testing.T) {
+	tests := []struct {
+		name     string
+		vertices int
+		edges    map[int][]int
+		expected [][]int
+	}{
+		{
+			name:     "Single SCC",
+			vertices: 5,
+			edges: map[int][]int{
+				0: {1},
+				1: {2},
+				2: {0, 3},
+				3: {4},
+				4: {},
+			},
+			expected: [][]int{{4}, {3}, {0, 2, 1}},
+		},
+		{
+			name:     "Multiple SCCs",
+			vertices: 8,
+			edges: map[int][]int{
+				0: {1},
+				1: {2},
+				2: {0, 3},
+				3: {4},
+				4: {5},
+				5: {3, 6},
+				6: {7},
+				7: {6},
+			},
+			expected: [][]int{{6, 7}, {3, 4, 5}, {0, 2, 1}},
+		},
+		{
+			name:     "Disconnected graph",
+			vertices: 4,
+			edges: map[int][]int{
+				0: {1},
+				1: {},
+				2: {3},
+				3: {},
+			},
+			expected: [][]int{{1}, {0}, {3}, {2}},
+		},
+		{
+			name:     "No edges",
+			vertices: 3,
+			edges: map[int][]int{
+				0: {},
+				1: {},
+				2: {},
+			},
+			expected: [][]int{{0}, {1}, {2}},
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			// Initializing graph
+			graph := &Graph{
+				vertices: tt.vertices,
+				edges:    make(map[int]map[int]int),
+			}
+			for v, neighbors := range tt.edges {
+				graph.edges[v] = make(map[int]int)
+				for _, neighbor := range neighbors {
+					graph.edges[v][neighbor] = 1
+				}
+			}
+
+			// Running Kosaraju's algorithm to get the SCCs
+			result := graph.Kosaraju()
+
+			// Sort the expected and result SCCs to ensure order doesn't matter
+			sortSlices(tt.expected)
+			sortSlices(result)
+
+			// Compare the sorted SCCs
+			if !reflect.DeepEqual(result, tt.expected) {
+				t.Errorf("expected %v, got %v", tt.expected, result)
+			}
+		})
+	}
+}
+
+// Utility function to sort the slices and their contents
+func sortSlices(s [][]int) {
+	for _, inner := range s {
+		sort.Ints(inner)
+	}
+	sort.Slice(s, func(i, j int) bool {
+		if len(s[i]) == 0 || len(s[j]) == 0 {
+			return len(s[i]) < len(s[j])
+		}
+		return s[i][0] < s[j][0]
+	})
+}