infomap算法java实现

Infomap算法是一种基于信息论的网络社区发现算法，可以用来识别网络中的社区结构。以下是一个基于Java语言实现Infomap算法的示例代码：

import java.util.*;

public class InfomapCommunityDetection {
    private static final double DEFAULT_TOLERANCE = 1e-9;
    private static final int DEFAULT_MAX_ITERATIONS = 1000;
    private static final double DEFAULT_DAMPING_FACTOR = 0.85;
    private static final double DEFAULT_TELEPORTATION_PROBABILITY = 0.15;

    public Map<String, Integer> detectCommunities(Map<String, Set<String>> graph) {
        Map<String, Integer> nodeToCommunity = new HashMap<>();
        List<String> nodes = new ArrayList<>(graph.keySet());
        int numNodes = nodes.size();
        double[][] transitionMatrix = calculateTransitionMatrix(graph, nodes);

        // Initialize node communities
        for (int i = 0; i < numNodes; i++) {
            nodeToCommunity.put(nodes.get(i), i);
        }

        // Run Infomap algorithm
        boolean converged = false;
        int iterations = 0;
        double[] p = new double[numNodes];
        double[] oldP = new double[numNodes];
        Arrays.fill(p, 1.0 / numNodes);
        while (!converged && iterations < DEFAULT_MAX_ITERATIONS) {
            // Save previous distribution
            System.arraycopy(p, 0, oldP, 0, numNodes);

            // Update distribution
            for (int i = 0; i < numNodes; i++) {
                double newP = 0.0;
                for (int j = 0; j < numNodes; j++) {
                    newP += transitionMatrix[j][i] * oldP[j];
                }
                p[i] = (1 - DEFAULT_DAMPING_FACTOR) / numNodes + DEFAULT_DAMPING_FACTOR * newP;
            }

            // Check for convergence
            double error = 0.0;
            for (int i = 0; i < numNodes; i++) {
                error += Math.abs(p[i] - oldP[i]);
            }
            converged = (error < DEFAULT_TOLERANCE);
            iterations++;
        }

        // Assign nodes to communities
        Map<Integer, List<String>> communityToNodes = new HashMap<>();
        for (int i = 0; i < numNodes; i++) {
            int community = 0;
            double maxP = Double.MIN_VALUE;
            for (int j = 0; j < numNodes; j++) {
                if (transitionMatrix[j][i] > 0 && p[j] > maxP) {
                    maxP = p[j];
                    community = nodeToCommunity.get(nodes.get(j));
                }
            }
            nodeToCommunity.put(nodes.get(i), community);
            if (!communityToNodes.containsKey(community)) {
                communityToNodes.put(community, new ArrayList<>());
            }
            communityToNodes.get(community).add(nodes.get(i));
        }

        return nodeToCommunity;
    }

    private double[][] calculateTransitionMatrix(Map<String, Set<String>> graph, List<String> nodes) {
        int numNodes = nodes.size();
        double[][] transitionMatrix = new double[numNodes][numNodes];

        // Calculate node degrees
        int[] nodeDegrees = new int[numNodes];
        for (int i = 0; i < numNodes; i++) {
            String node = nodes.get(i);
            nodeDegrees[i] = graph.get(node).size();
        }

        // Calculate transition probabilities
        for (int i = 0; i < numNodes; i++) {
            String node1 = nodes.get(i);
            for (int j = 0; j < numNodes; j++) {
                String node2 = nodes.get(j);
                Set<String> neighbors = graph.get(node1);
                double transitionProbability = 0.0;
                if (neighbors.contains(node2)) {
                    transitionProbability = 1.0 / nodeDegrees[i];
                }
                transitionMatrix[i][j] = (1 - DEFAULT_TELEPORTATION_PROBABILITY) * transitionProbability +
                        DEFAULT_TELEPORTATION_PROBABILITY / numNodes;
            }
        }

        return transitionMatrix;
    }
}

这段代码实现了一个Infomap社区检测算法，对于输入的图数据，返回每个节点所属的社区编号。具体实现中，我们首先计算了转移概率矩阵，然后使用随机游走的方式计算节点的分布，最后根据节点分布将节点分配到对应的社区中。