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Connected components are not merely an academic exercise—they're a workhorse algorithm with applications spanning virtually every domain where networks appear. From analyzing social connections to detecting objects in images to ensuring infrastructure reliability, component finding solves real problems at scale.
This page bridges the gap between algorithmic theory and practical application. We'll explore diverse use cases, implement domain-specific solutions, and understand why connected components are often the first algorithm applied when analyzing any graph or network structure.
By the end of this page, you'll understand how connected components power social network analysis, image segmentation, network reliability testing, database query optimization, and more. You'll see concrete implementations for several domains and develop intuition for recognizing component-based opportunities in your own work.
Social networks—Facebook, LinkedIn, Twitter/X, Discord servers—are graphs where vertices are users and edges represent relationships (friendship, following, membership). Connected components reveal the community structure of these networks.
What Components Tell Us:
Isolated Users: Components of size 1 identify users with no connections—potential targets for engagement campaigns or signs of fake/inactive accounts.
Community Detection: Each component is a naturally isolated community. Users within a component can reach each other (through friend-of-friend chains), but cannot reach users in other components.
Network Health: A healthy social network typically has one giant component containing most users. Many small components might indicate fragmentation or siloed communities.
Viral Spread Potential: Information (or misinformation) can only spread within a component. The giant component determines maximum viral reach.
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interface SocialNetworkStats { totalUsers: number; connectedUsers: number; // Users in components of size > 1 isolatedUsers: number; // Users with no connections communities: number; // Number of components giantComponentSize: number; // Largest component giantComponentPct: number; // % of users in giant component avgCommunitySize: number; // Average component size reachability: number; // Fraction of user pairs that can reach each other} class SocialNetworkAnalyzer { private userConnections: Map<string, string[]>; private componentId: Map<string, number>; private componentSizes: number[]; constructor(connections: [string, string][]) { this.userConnections = new Map(); this.componentId = new Map(); this.componentSizes = []; // Build adjacency list for (const [user1, user2] of connections) { if (!this.userConnections.has(user1)) this.userConnections.set(user1, []); if (!this.userConnections.has(user2)) this.userConnections.set(user2, []); this.userConnections.get(user1)!.push(user2); this.userConnections.get(user2)!.push(user1); } this.computeComponents(); } addUser(userId: string): void { if (!this.userConnections.has(userId)) { this.userConnections.set(userId, []); } } private computeComponents(): void { let currentComponent = 0; const dfs = (user: string, compId: number): number => { this.componentId.set(user, compId); let size = 1; for (const friend of this.userConnections.get(user) || []) { if (!this.componentId.has(friend)) { size += dfs(friend, compId); } } return size; }; for (const user of this.userConnections.keys()) { if (!this.componentId.has(user)) { const size = dfs(user, currentComponent); this.componentSizes.push(size); currentComponent++; } } } analyze(): SocialNetworkStats { const totalUsers = this.userConnections.size; const isolated = this.componentSizes.filter(s => s === 1).length; const connected = totalUsers - isolated; const giantSize = Math.max(...this.componentSizes, 0); // Reachability: fraction of pairs in same component // = sum of (size choose 2) / (total choose 2) let reachablePairs = 0; for (const size of this.componentSizes) { if (size >= 2) { reachablePairs += (size * (size - 1)) / 2; } } const totalPairs = (totalUsers * (totalUsers - 1)) / 2; const reachability = totalPairs > 0 ? reachablePairs / totalPairs : 0; return { totalUsers, connectedUsers: connected, isolatedUsers: isolated, communities: this.componentSizes.length, giantComponentSize: giantSize, giantComponentPct: totalUsers > 0 ? (giantSize / totalUsers) * 100 : 0, avgCommunitySize: totalUsers / this.componentSizes.length, reachability }; } canReach(user1: string, user2: string): boolean { return this.componentId.get(user1) === this.componentId.get(user2); } getCommunityMembers(userId: string): string[] { const compId = this.componentId.get(userId); if (compId === undefined) return []; const members: string[] = []; for (const [user, id] of this.componentId.entries()) { if (id === compId) members.push(user); } return members; } getIsolatedUsers(): string[] { return Array.from(this.componentId.entries()) .filter(([user, id]) => this.componentSizes[id] === 1 ) .map(([user]) => user); }}In most successful social networks, 80-95% of active users are in a single giant component. This means almost any user can reach almost any other through friend-of-friend chains. When this doesn't hold—when the network is fragmented—it often signals problems or niche communities.
One of the most elegant applications of connected components is in image processing. Images are naturally graphs: pixels are vertices, and adjacent pixels can be connected based on similarity (color, intensity).
The Image-as-Graph Model:
Applications:
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interface Region { id: number; pixels: [number, number][]; size: number; boundingBox: { minRow: number; maxRow: number; minCol: number; maxCol: number };} /** * Find connected components in a binary image * Uses 4-connectivity (up, down, left, right) */class ImageSegmenter { private image: number[][]; private rows: number; private cols: number; private labels: number[][]; private regions: Region[] = []; constructor(image: number[][]) { this.image = image; this.rows = image.length; this.cols = image[0]?.length || 0; this.labels = Array.from({ length: this.rows }, () => new Array(this.cols).fill(-1)); this.findComponents(); } private findComponents(): void { let currentLabel = 0; for (let row = 0; row < this.rows; row++) { for (let col = 0; col < this.cols; col++) { // Only process foreground pixels (value 1) that aren't labeled if (this.image[row][col] === 1 && this.labels[row][col] === -1) { const pixels = this.bfs(row, col, currentLabel); this.regions.push(this.createRegion(currentLabel, pixels)); currentLabel++; } } } } private bfs(startRow: number, startCol: number, label: number): [number, number][] { const pixels: [number, number][] = []; const queue: [number, number][] = [[startRow, startCol]]; this.labels[startRow][startCol] = label; // 4-way connectivity const directions = [[-1, 0], [1, 0], [0, -1], [0, 1]]; while (queue.length > 0) { const [row, col] = queue.shift()!; pixels.push([row, col]); for (const [dr, dc] of directions) { const newRow = row + dr; const newCol = col + dc; if (this.isValid(newRow, newCol) && this.image[newRow][newCol] === 1 && this.labels[newRow][newCol] === -1) { this.labels[newRow][newCol] = label; queue.push([newRow, newCol]); } } } return pixels; } private isValid(row: number, col: number): boolean { return row >= 0 && row < this.rows && col >= 0 && col < this.cols; } private createRegion(id: number, pixels: [number, number][]): Region { let minRow = Infinity, maxRow = -Infinity; let minCol = Infinity, maxCol = -Infinity; for (const [row, col] of pixels) { minRow = Math.min(minRow, row); maxRow = Math.max(maxRow, row); minCol = Math.min(minCol, col); maxCol = Math.max(maxCol, col); } return { id, pixels, size: pixels.length, boundingBox: { minRow, maxRow, minCol, maxCol } }; } getRegions(): Region[] { return this.regions; } getLabeledImage(): number[][] { return this.labels; } getRegionCount(): number { return this.regions.length; } getLargestRegion(): Region | null { return this.regions.reduce<Region | null>((max, region) => !max || region.size > max.size ? region : max, null); } filterBySize(minSize: number): Region[] { return this.regions.filter(r => r.size >= minSize); }}Example: Counting Objects in Binary Image
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// Example binary image (1 = foreground, 0 = background)const binaryImage = [ [0, 1, 1, 0, 0, 0, 1, 1], [0, 1, 1, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 1, 1, 1, 0], [1, 1, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0],]; const segmenter = new ImageSegmenter(binaryImage); console.log(`Found ${segmenter.getRegionCount()} distinct objects`);// Output: Found 4 distinct objects for (const region of segmenter.getRegions()) { console.log(`Region ${region.id}: ${region.size} pixels, ` + `bounding box: (${region.boundingBox.minRow},${region.boundingBox.minCol}) to ` + `(${region.boundingBox.maxRow},${region.boundingBox.maxCol})`);} // Filter noise (small regions)const significantRegions = segmenter.filterBySize(3);console.log(`${significantRegions.length} regions with size >= 3`);4-connectivity considers only horizontal/vertical neighbors. 8-connectivity also includes diagonals. The choice affects what counts as 'connected.' Diagonally touching pixels are separate objects in 4-connectivity but together in 8-connectivity. Choose based on your application's needs.
Computer networks, power grids, transportation systems, and communication infrastructure are all graphs. Connected components analysis helps ensure reliability and availability of these critical systems.
Critical Questions Components Answer:
Can all nodes communicate? A network should ideally be fully connected—one component.
What happens if a link fails? Does the network split into multiple components?
Which nodes become isolated? After failures, which nodes can no longer reach the rest?
How resilient is the network? Does removing any single edge disconnect the network?
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interface FailureImpact { originalComponents: number; afterFailure: number; isolatedNodes: string[]; newComponents: string[][]; criticalLink: boolean; // Did this link disconnect the network?} class NetworkReliabilityAnalyzer { private network: Map<string, Set<string>>; constructor(links: [string, string][]) { this.network = new Map(); for (const [node1, node2] of links) { if (!this.network.has(node1)) this.network.set(node1, new Set()); if (!this.network.has(node2)) this.network.set(node2, new Set()); this.network.get(node1)!.add(node2); this.network.get(node2)!.add(node1); } } isFullyConnected(): boolean { return this.countComponents() === 1; } countComponents(): number { const visited = new Set<string>(); let count = 0; for (const node of this.network.keys()) { if (!visited.has(node)) { this.dfs(node, visited); count++; } } return count; } private dfs(node: string, visited: Set<string>): void { visited.add(node); for (const neighbor of this.network.get(node) || []) { if (!visited.has(neighbor)) { this.dfs(neighbor, visited); } } } // Analyze impact of removing a specific link simulateLinkFailure(node1: string, node2: string): FailureImpact { const originalComponents = this.countComponents(); // Temporarily remove the link this.network.get(node1)?.delete(node2); this.network.get(node2)?.delete(node1); // Analyze new state const newComponentCount = this.countComponents(); const components = this.getAllComponents(); // Find isolated nodes (in components without critical services) const isolatedNodes = components .filter(c => c.length === 1) .map(c => c[0]); // Restore the link this.network.get(node1)?.add(node2); this.network.get(node2)?.add(node1); return { originalComponents, afterFailure: newComponentCount, isolatedNodes, newComponents: components, criticalLink: newComponentCount > originalComponents }; } // Find all links whose removal would disconnect the network findCriticalLinks(): [string, string][] { const criticalLinks: [string, string][] = []; const visited = new Set<string>(); for (const [node, neighbors] of this.network) { for (const neighbor of neighbors) { const linkKey = [node, neighbor].sort().join('-'); if (visited.has(linkKey)) continue; visited.add(linkKey); const impact = this.simulateLinkFailure(node, neighbor); if (impact.criticalLink) { criticalLinks.push([node, neighbor]); } } } return criticalLinks; } private getAllComponents(): string[][] { const visited = new Set<string>(); const components: string[][] = []; for (const node of this.network.keys()) { if (!visited.has(node)) { const component: string[] = []; this.dfsCollect(node, visited, component); components.push(component); } } return components; } private dfsCollect(node: string, visited: Set<string>, component: string[]): void { visited.add(node); component.push(node); for (const neighbor of this.network.get(node) || []) { if (!visited.has(neighbor)) { this.dfsCollect(neighbor, visited, component); } } }}Example: Analyzing a Data Center Network
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// Data center network topologyconst datacenterLinks: [string, string][] = [ ['router-main', 'switch-1'], ['router-main', 'switch-2'], ['switch-1', 'server-a'], ['switch-1', 'server-b'], ['switch-2', 'server-c'], ['switch-2', 'server-d'], ['switch-1', 'switch-2'], // Redundant link]; const analyzer = new NetworkReliabilityAnalyzer(datacenterLinks); console.log(`Network connected: ${analyzer.isFullyConnected()}`);// Output: Network connected: true const criticalLinks = analyzer.findCriticalLinks();console.log(`Critical links (removal disconnects network):`);for (const [a, b] of criticalLinks) { console.log(` ${a} <-> ${b}`);}// Critical links depend on topology - main router links are typically critical // What if the router-main to switch-1 link fails?const impact = analyzer.simulateLinkFailure('router-main', 'switch-1');console.log(`Link failure impact:`);console.log(` Components after: ${impact.afterFailure}`);console.log(` Critical link: ${impact.criticalLink}`);Links whose removal disconnects the network are called 'bridges.' Similarly, nodes whose removal disconnects the network are 'articulation points.' These are critical infrastructure elements requiring redundancy. Finding them efficiently uses DFS with discovery/low times (covered in Chapter 24).
Connected components optimize database operations in several ways, particularly in distributed systems and query planning.
1. Independent Partition Identification
In sharded databases, data that never joins together can be stored independently. Connected components identify these independent partitions.
2. Query Graph Analysis
Complex queries can be represented as graphs where vertices are tables and edges are joins. If the query graph has multiple components, each can be executed separately (potentially in parallel).
3. Equivalence Class Detection
When columns must equal each other (e.g., a.id = b.id AND b.id = c.id), transitivity creates equivalence classes. Connected components find these efficiently.
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interface QueryComponent { tables: string[]; joins: [string, string][]; canExecuteIndependently: boolean;} class QueryOptimizer { /** * Analyze a query's join graph to find independent subqueries */ static analyzeJoinGraph( tables: string[], joinConditions: [string, string][] ): QueryComponent[] { // Build adjacency list const graph = new Map<string, string[]>(); for (const table of tables) { graph.set(table, []); } for (const [t1, t2] of joinConditions) { graph.get(t1)?.push(t2); graph.get(t2)?.push(t1); } // Find connected components const tableToComponent = new Map<string, number>(); const components: QueryComponent[] = []; let compId = 0; const dfs = (table: string, componentTables: string[]): void => { tableToComponent.set(table, compId); componentTables.push(table); for (const neighbor of graph.get(table) || []) { if (!tableToComponent.has(neighbor)) { dfs(neighbor, componentTables); } } }; for (const table of tables) { if (!tableToComponent.has(table)) { const componentTables: string[] = []; dfs(table, componentTables); // Get joins within this component const componentJoins = joinConditions.filter( ([t1, t2]) => tableToComponent.get(t1) === compId && tableToComponent.get(t2) === compId ); components.push({ tables: componentTables, joins: componentJoins, canExecuteIndependently: true }); compId++; } } return components; } /** * Find equivalence classes in equality conditions * e.g., a.x = b.y AND b.y = c.z => {a.x, b.y, c.z} are equivalent */ static findEquivalenceClasses( equalityConditions: [string, string][] ): string[][] { const graph = new Map<string, string[]>(); const allColumns = new Set<string>(); for (const [col1, col2] of equalityConditions) { allColumns.add(col1); allColumns.add(col2); if (!graph.has(col1)) graph.set(col1, []); if (!graph.has(col2)) graph.set(col2, []); graph.get(col1)!.push(col2); graph.get(col2)!.push(col1); } const visited = new Set<string>(); const equivalenceClasses: string[][] = []; const dfs = (col: string, eqClass: string[]): void => { visited.add(col); eqClass.push(col); for (const equiv of graph.get(col) || []) { if (!visited.has(equiv)) { dfs(equiv, eqClass); } } }; for (const col of allColumns) { if (!visited.has(col)) { const eqClass: string[] = []; dfs(col, eqClass); equivalenceClasses.push(eqClass); } } return equivalenceClasses; }} // Example: Analyze a complex queryconst tables = ['users', 'orders', 'items', 'categories', 'logs', 'metrics'];const joins: [string, string][] = [ ['users', 'orders'], ['orders', 'items'], ['items', 'categories'], ['logs', 'metrics'], // Independent subquery!]; const components = QueryOptimizer.analyzeJoinGraph(tables, joins);console.log(`Query has ${components.length} independent components`);// Output: Query has 2 independent components// Component 1: users-orders-items-categories// Component 2: logs-metricsWhen a query has multiple independent components, each can execute in parallel. This is exactly how query optimizers in databases like PostgreSQL and MySQL identify parallelization opportunities—through connected component analysis of the join graph.
Games frequently use graph structures for maps, networks, and relationships. Connected components solve several game development challenges.
Common Game Applications:
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interface Match { color: string; cells: [number, number][]; size: number;} class Match3Detector { private grid: string[][]; private rows: number; private cols: number; constructor(grid: string[][]) { this.grid = grid; this.rows = grid.length; this.cols = grid[0]?.length || 0; } /** * Find all groups of 3+ connected same-colored pieces */ findMatches(minSize: number = 3): Match[] { const visited: boolean[][] = Array.from({ length: this.rows }, () => new Array(this.cols).fill(false)); const matches: Match[] = []; for (let row = 0; row < this.rows; row++) { for (let col = 0; col < this.cols; col++) { if (!visited[row][col] && this.grid[row][col] !== '.') { const color = this.grid[row][col]; const cells = this.bfsCollect(row, col, color, visited); if (cells.length >= minSize) { matches.push({ color, cells, size: cells.length }); } } } } return matches; } private bfsCollect( startRow: number, startCol: number, color: string, visited: boolean[][] ): [number, number][] { const cells: [number, number][] = []; const queue: [number, number][] = [[startRow, startCol]]; visited[startRow][startCol] = true; const directions = [[-1, 0], [1, 0], [0, -1], [0, 1]]; while (queue.length > 0) { const [row, col] = queue.shift()!; cells.push([row, col]); for (const [dr, dc] of directions) { const newRow = row + dr; const newCol = col + dc; if (this.isValid(newRow, newCol) && !visited[newRow][newCol] && this.grid[newRow][newCol] === color) { visited[newRow][newCol] = true; queue.push([newRow, newCol]); } } } return cells; } private isValid(row: number, col: number): boolean { return row >= 0 && row < this.rows && col >= 0 && col < this.cols; } /** * Remove matched cells and let pieces fall */ applyGravity(): void { for (let col = 0; col < this.cols; col++) { // Collect non-empty cells in this column const pieces: string[] = []; for (let row = this.rows - 1; row >= 0; row--) { if (this.grid[row][col] !== '.') { pieces.push(this.grid[row][col]); } } // Place them at the bottom for (let row = this.rows - 1; row >= 0; row--) { const pieceIndex = this.rows - 1 - row; this.grid[row][col] = pieceIndex < pieces.length ? pieces[pieceIndex] : '.'; } } } removeMatches(matches: Match[]): void { for (const match of matches) { for (const [row, col] of match.cells) { this.grid[row][col] = '.'; } } }} // Example game gridconst gameGrid = [ ['R', 'B', 'R', 'R'], ['R', 'R', 'B', 'G'], ['G', 'R', 'G', 'G'], ['G', 'G', 'R', 'G'],]; const detector = new Match3Detector(gameGrid);const matches = detector.findMatches(3); console.log(`Found ${matches.length} matches:`);for (const match of matches) { console.log(` ${match.color}: ${match.size} pieces at ${JSON.stringify(match.cells)}`);}In match-3 games, removing matches and applying gravity can create new matches (chain reactions). The algorithm: find matches → remove → apply gravity → repeat until no matches. Each iteration uses connected components to find the new matches.
Biological systems are naturally modeled as graphs: protein interaction networks, metabolic pathways, gene regulatory networks, phylogenetic trees. Connected components reveal critical biological insights.
Applications in Biology:
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interface ProteinComplex { id: number; proteins: string[]; size: number; averageInteractionScore: number;} class ProteinInteractionAnalyzer { private network: Map<string, Map<string, number>>; // protein -> neighbor -> score constructor(interactions: [string, string, number][]) { this.network = new Map(); for (const [protein1, protein2, score] of interactions) { if (!this.network.has(protein1)) this.network.set(protein1, new Map()); if (!this.network.has(protein2)) this.network.set(protein2, new Map()); this.network.get(protein1)!.set(protein2, score); this.network.get(protein2)!.set(protein1, score); } } /** * Find protein complexes (connected components with score threshold) */ findComplexes(minInteractionScore: number = 0): ProteinComplex[] { const visited = new Set<string>(); const complexes: ProteinComplex[] = []; let complexId = 0; for (const protein of this.network.keys()) { if (!visited.has(protein)) { const complex = this.bfsComplex(protein, minInteractionScore, visited); if (complex.proteins.length > 0) { complex.id = complexId++; complexes.push(complex); } } } return complexes; } private bfsComplex( start: string, minScore: number, visited: Set<string> ): ProteinComplex { const proteins: string[] = []; const queue: string[] = [start]; visited.add(start); let totalScore = 0; let edgeCount = 0; while (queue.length > 0) { const protein = queue.shift()!; proteins.push(protein); for (const [neighbor, score] of this.network.get(protein) || []) { if (score >= minScore) { totalScore += score; edgeCount++; if (!visited.has(neighbor)) { visited.add(neighbor); queue.push(neighbor); } } } } return { id: 0, proteins, size: proteins.length, averageInteractionScore: edgeCount > 0 ? totalScore / edgeCount : 0 }; } /** * Find potential drug targets * Proteins in large complexes with high connectivity are often essential */ findEssentialProteins(): string[] { const complexes = this.findComplexes(0.5); const essentials: string[] = []; for (const complex of complexes) { if (complex.size >= 5) { // Find hub proteins (high degree within complex) const maxConnections = Math.max( ...complex.proteins.map(p => this.network.get(p)?.size || 0 ) ); for (const protein of complex.proteins) { if ((this.network.get(protein)?.size || 0) === maxConnections) { essentials.push(protein); } } } } return essentials; }} // Example: Yeast protein interactions (simplified)const interactions: [string, string, number][] = [ ['CDC28', 'CLB1', 0.9], ['CDC28', 'CLB2', 0.9], ['CLB1', 'CLB2', 0.7], ['ACT1', 'ARP2', 0.8], ['ACT1', 'ARP3', 0.8], ['ARP2', 'ARP3', 0.9], ['MDH1', 'MDH2', 0.6], // Separate complex]; const analyzer = new ProteinInteractionAnalyzer(interactions);const complexes = analyzer.findComplexes(0.5); console.log(`Found ${complexes.length} protein complexes`);for (const complex of complexes) { console.log(` Complex ${complex.id}: ${complex.proteins.join(', ')}`);}In biological networks, edges often have confidence scores. By varying the score threshold, you get different component structures—high thresholds reveal core complexes, low thresholds reveal extended functional modules. This 'filtration' perspective connects to persistent homology in topological data analysis.
Connected components appear in countless other domains. Here's a survey of additional applications:
| Domain | Graph Model | Components Represent | Business Value |
|---|---|---|---|
| Fraud Detection | Users connected by shared attributes | Fraud rings | Identify organized fraud networks |
| Supply Chain | Suppliers connected by shared products | Independent supply chains | Risk assessment, redundancy planning |
| Recommendation Systems | Items connected by co-purchase | Item clusters | Recommendation diversity |
| Geographic Mapping | Locations connected by roads | Isolated regions | Infrastructure planning |
| Citation Networks | Papers connected by citations | Research communities | Identify research silos |
| Email Analysis | Addresses connected by emails | Communication groups | Org structure analysis |
| Electrical Circuits | Components connected by wires | Independent circuits | Circuit analysis |
| Chemistry | Atoms connected by bonds | Molecules | Compound identification |
Whenever you have entities with binary relationships (connected or not), you have a graph. Whenever you need to understand the structure of such relationships—what's grouped together, what's isolated, what can reach what—connected components are your first tool. Learn to see graphs everywhere.
We've journeyed from the mathematical foundations of connected components through algorithms to real-world applications. Let's consolidate everything we've learned.
The Bigger Picture:
Connected components are typically the first algorithm applied when analyzing any graph. Before shortest paths, before spanning trees, before flow algorithms—you need to understand basic connectivity. This module has equipped you with that foundation.
What's Next:
The subsequent modules in this chapter build on connectivity concepts:
Each assumes you can identify or work within connected regions—exactly what you've now mastered.
Congratulations! You've mastered connected components in undirected graphs—a fundamental graph algorithm with applications across every domain that uses networks. You can now find components, query connectivity instantly, analyze network structure, and recognize when component analysis solves real problems. This knowledge forms the foundation for all advanced graph algorithms.