Example Implementations

/**
 * PageRank algorithm implementation with generic key type support
 * Uses scatter-gather pattern for distributed computation
 * @param <K> Vertex key type
 */
public class PageRank<K> {
    /** 
     * Create PageRank instance with specified parameters
     * @param beta Damping factor (typically 0.85)
     * @param maxIterations Maximum number of iterations
     */
    public PageRank(double beta, int maxIterations);
    
    /**
     * Execute PageRank algorithm on input graph
     * @param network Input graph with double-valued vertices and edges
     * @return DataSet of vertices with PageRank scores
     */
    public DataSet<Vertex<K, Double>> run(Graph<K, Double, Double> network);
}

import org.apache.flink.graph.examples.PageRank;

// Create PageRank instance
PageRank<Long> pageRank = new PageRank<>(0.85, 10);

// Prepare graph with initial values
Graph<Long, Double, Double> graph = inputGraph
    .mapVertices(new MapFunction<Vertex<Long, NullValue>, Double>() {
        public Double map(Vertex<Long, NullValue> vertex) {
            return 1.0;  // Initial PageRank value
        }
    });

// Execute algorithm
DataSet<Vertex<Long, Double>> results = pageRank.run(graph);
results.print();

/**
 * Single Source Shortest Paths algorithm
 * Computes shortest distances from a source vertex to all reachable vertices
 */
public class SingleSourceShortestPaths {
    /**
     * Execute SSSP from specified source vertex
     * @param graph Input graph with numeric edge weights
     * @param srcVertexId Source vertex identifier
     * @param maxIterations Maximum number of iterations
     * @return DataSet of vertices with shortest distances
     */
    public static DataSet<Vertex<Long, Double>> run(
        Graph<Long, NullValue, Double> graph, 
        Long srcVertexId, 
        Integer maxIterations
    ) throws Exception;
}

// Execute SSSP from vertex 1
DataSet<Vertex<Long, Double>> distances = 
    SingleSourceShortestPaths.run(weightedGraph, 1L, 10);

// Filter reachable vertices
DataSet<Vertex<Long, Double>> reachable = distances
    .filter(vertex -> vertex.getValue() < Double.POSITIVE_INFINITY);

/**
 * PageRank implementation using Gather-Sum-Apply pattern
 */
public class GSAPageRank {
    public static void main(String[] args) throws Exception;
    
    /** Custom gather function for PageRank computation */
    public static class GatherRanks implements GatherFunction<Double, Double, Double>;
    
    /** Custom sum function for aggregating ranks */
    public static class SumRanks implements SumFunction<Double, Double, Double>;
    
    /** Custom apply function for updating vertex values */
    public static class UpdateRanks implements ApplyFunction<Long, Double, Double>;
}

/**
 * SSSP implementation using Gather-Sum-Apply pattern
 */
public class GSASingleSourceShortestPaths {
    public static void main(String[] args) throws Exception;
    
    /** Gather minimum distances from neighbors */
    public static class GatherDistances implements GatherFunction<Double, Double, Double>;
    
    /** Select minimum distance */
    public static class SelectMinDistance implements SumFunction<Double, Double, Double>;
    
    /** Update vertex distance if improvement found */
    public static class UpdateDistance implements ApplyFunction<Long, Double, Double>;
}

/**
 * Incremental Single Source Shortest Paths
 * Efficiently recomputes shortest paths when graph structure changes
 */
public class IncrementalSSSP {
    public static void main(String[] args) throws Exception;
    
    /**
     * Process incremental updates to shortest path tree
     * @param graph Base graph with current distances
     * @param edgeUpdates Stream of edge additions/deletions
     * @return Updated shortest path tree
     */
    public static DataSet<Vertex<Long, Double>> processUpdates(
        Graph<Long, Double, Double> graph,
        DataSet<Edge<Long, Double>> edgeUpdates
    ) throws Exception;
}

/**
 * Music recommendation system using graph processing
 * Builds user-song bipartite graph and computes recommendations
 */
public class MusicProfiles {
    public static void main(String[] args) throws Exception;
    
    /**
     * Generate music recommendations based on user listening history
     * @param userSongGraph Bipartite graph of users and songs
     * @param targetUser User to generate recommendations for
     * @return Ranked list of song recommendations
     */
    public static DataSet<Tuple2<String, Double>> generateRecommendations(
        Graph<String, NullValue, Double> userSongGraph,
        String targetUser
    ) throws Exception;
}

/**
 * Weight graph edges by Euclidean distance between vertex coordinates
 * Useful for spatial networks and geographic data processing
 */
public class EuclideanGraphWeighing {
    public static void main(String[] args) throws Exception;
    
    /**
     * Compute Euclidean distances for graph edges
     * @param graph Graph with vertex coordinates
     * @return Graph with distance-weighted edges
     */
    public static Graph<Long, Point, Double> computeDistances(
        Graph<Long, Point, NullValue> graph
    ) throws Exception;
}

/**
 * Single Source Shortest Paths using Pregel model
 * Demonstrates vertex-centric programming approach
 */
public class PregelSSSP {
    public static void main(String[] args) throws Exception;
    
    /** Pregel compute function for SSSP */
    public static class SSSPComputeFunction extends ComputeFunction<Long, Double, Double, Double>;
    
    /** Message combiner for efficiency */
    public static class MinMessageCombiner implements MessageCombiner<Long, Double>;
}

/**
 * Connected Components implementation in Scala
 * Demonstrates functional programming style with Flink Scala API
 */
object ConnectedComponents {
  def main(args: Array[String]): Unit
  
  /**
   * Find connected components using label propagation
   * @param graph Input graph
   * @param maxIterations Maximum iterations
   * @return Components as vertex-component pairs
   */
  def findComponents(
    graph: Graph[Long, Long, NullValue], 
    maxIterations: Int
  ): DataSet[(Long, Long)]
}

/**
 * GSA Single Source Shortest Paths in Scala
 * Functional implementation of gather-sum-apply pattern
 */
object GSASingleSourceShortestPaths {
  def main(args: Array[String]): Unit
  
  /**
   * Compute shortest paths using functional GSA approach
   * @param graph Weighted graph
   * @param srcVertex Source vertex
   * @return Shortest distances from source
   */
  def computeShortestPaths(
    graph: Graph[Long, Double, Double],
    srcVertex: Long
  ): DataSet[(Long, Double)]
}

/**
 * Standard SSSP implementation in Scala
 * Demonstrates idiomatic Scala programming with Flink
 */
object SingleSourceShortestPaths {
  def main(args: Array[String]): Unit
  
  // Functional vertex update operations
  def updateDistance(vertex: Vertex[Long, Double], messages: Iterator[Double]): Double
  
  // Distance initialization
  def initializeDistances(srcVertex: Long): MapFunction[Vertex[Long, NullValue], Double]
}

/**
 * Default datasets for PageRank testing
 */
public class PageRankData {
    /** Get default vertex dataset */
    public static DataSet<Vertex<Long, Double>> getDefaultVertexDataSet(ExecutionEnvironment env);
    
    /** Get default edge dataset */
    public static DataSet<Edge<Long, Double>> getDefaultEdgeDataSet(ExecutionEnvironment env);
    
    /** Expected PageRank results for validation */
    public static String getExpectedResult();
}

/**
 * Test data for connected components algorithms
 */
public class ConnectedComponentsDefaultData {
    public static DataSet<Vertex<Long, Long>> getDefaultVertexDataSet(ExecutionEnvironment env);
    public static DataSet<Edge<Long, NullValue>> getDefaultEdgeDataSet(ExecutionEnvironment env);
    public static String getExpectedResult();
}

/**
 * Spatial graph data for Euclidean distance examples
 */
public class EuclideanGraphData {
    /** Vertices with 2D coordinates */
    public static DataSet<Vertex<Long, Point>> getDefaultVertexDataSet(ExecutionEnvironment env);
    
    /** Edges for spatial connectivity */
    public static DataSet<Edge<Long, NullValue>> getDefaultEdgeDataSet(ExecutionEnvironment env);
}

/**
 * Music recommendation test data
 */
public class MusicProfilesData {
    /** User-song interaction data */
    public static DataSet<Tuple3<String, String, Double>> getUserSongRatings(ExecutionEnvironment env);
    
    /** Expected recommendation results */
    public static String getExpectedResult();
}

// Direct execution of example algorithms
public static void main(String[] args) throws Exception {
    ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    
    // Create test graph
    Graph<Long, NullValue, Double> graph = Graph.fromDataSet(
        vertices, edges, env
    );
    
    // Run algorithm
    DataSet<Vertex<Long, Double>> result = 
        SingleSourceShortestPaths.run(graph, 1L, 10);
    
    // Print results
    result.print();
}

// Use examples as reference for custom drivers
public class CustomPageRankDriver extends DriverBase<Long, NullValue, Double> {
    private PageRank<Long> pageRankImpl;
    
    @Override
    public DataSet plan(Graph<Long, NullValue, Double> graph) throws Exception {
        // Initialize PageRank with parameters
        pageRankImpl = new PageRank<>(
            dampingFactor.getValue(),
            iterations.getValue()
        );
        
        // Convert graph format and execute
        Graph<Long, Double, Double> initializedGraph = 
            graph.mapVertices(vertex -> 1.0);
            
        return pageRankImpl.run(initializedGraph);
    }
}

// Use data classes for consistent testing
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

// Get standard test data
DataSet<Vertex<Long, Double>> vertices = PageRankData.getDefaultVertexDataSet(env);
DataSet<Edge<Long, Double>> edges = PageRankData.getDefaultEdgeDataSet(env);
Graph<Long, Double, Double> testGraph = Graph.fromDataSet(vertices, edges, env);

// Run algorithm
PageRank<Long> algorithm = new PageRank<>(0.85, 10);
DataSet<Vertex<Long, Double>> results = algorithm.run(testGraph);

// Validate against expected results
String expected = PageRankData.getExpectedResult();
String actual = DataSetUtils.collect(results).toString();
assertEquals(expected, actual);