AVRank, HVRank的实现

最新推荐文章于 2025-08-11 20:54:36 发布

转载最新推荐文章于 2025-08-11 20:54:36 发布 · 133 阅读

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#java

本文介绍了一种基于链接结构的反垃圾邮件算法CPV，并使用Java和webgraph库实现了该算法。通过设置不同的参数，计算出AVRank和HVRank，用于识别网络中的垃圾邮件发送者。

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看了论文《Deeply Exploiting Link Structure: Setting a Tougher Life for Spammers》，用Java和webgraph库实现了一下其中的算法CPV，即计算AVRank和HVRank。最终的结果没有做归一化处理，我用数据集试了一下，发现效果还不错。

代码实现如下：

package cn.edu.dlut.wisdom;

import it.unimi.dsi.webgraph.*;

public class Cpv {
    private ImmutableGraph graph;
    private ImmutableGraph igraph;
    private double alpha = 0.5;
    private double beta = 0.5;
    private double dampening = 0.85;
    private int numNodes;
    private int m = 50;

    public void setM(int m) {
        this.m = m;
    }

    public void setAlpha(double alpha) {
        this.alpha = alpha;
    }

    public void setBeta(double beta) {
        this.beta = beta;
    }

    public void setDampening(double dampening) {
        this.dampening = dampening;
    }

    public void setGraph(ImmutableGraph graph) {
        this.graph = graph;
    }

    public void setIAVRank(double[] iAVRank) {
        this.iAVRank = iAVRank;
    }

    public void setIgraph(ImmutableGraph igraph) {
        this.igraph = igraph;
    }

    public void setIHVRank(double[] iHVRank) {
        this.iHVRank = iHVRank;
    }
    private double[] iAVRank;
    private double[] iHVRank;
    private double[] aVRank;
    private double[] hVRank;

    public double[] getAVRank() {
        return aVRank;
    }

    public double[] getHvRank() {
        return hVRank;
    }

    public Cpv(ImmutableGraph graph, ImmutableGraph igraph, double [] iAVRank, double[] iHVRank, double alpha, double beta, double dampening, int m) {
        this.iAVRank = iAVRank;
        this.iHVRank = iHVRank;
        this.alpha = alpha;
        this.beta = beta;
        this.dampening = dampening;
        this.m = m;
        this.graph = graph;
        this.igraph = igraph;
        numNodes = graph.numNodes();
    }

    public Cpv(ImmutableGraph graph, ImmutableGraph igraph) {
        this.graph = graph;
        this.igraph = igraph;
        numNodes = graph.numNodes();
        iAVRank = new double[numNodes];
        iHVRank = new double[numNodes];
        double temp = 1.0 / numNodes;
        for(int i = 0; i < numNodes; i++) {
            iAVRank[i] = temp;
            iHVRank[i] = temp;
        }
    }

    public Cpv(ImmutableGraph graph) {
        this.graph = graph;
        igraph = Transform.transpose(graph);
        numNodes = graph.numNodes();
        iAVRank = new double[numNodes];
        iHVRank = new double[numNodes];
        double temp = 1.0 / numNodes;
        for(int i = 0; i < numNodes; i++) {
            iAVRank[i] = temp;
            iHVRank[i] = temp;
        }
    }

    public void computeRank() {
        // 初始化
        aVRank = iAVRank.clone();
        hVRank = iHVRank.clone();
        
        int iter = m;
        // 迭代
        while(iter-- > 0) {
            for(int i = 0; i < numNodes; i++) {
                if(igraph.outdegree(i) > 0) {
                    aVRank[i] = 0;
                    for(int j : igraph.successorArray(i)) {
                        int outDegree = graph.outdegree(j);
                        aVRank[i] += alpha * aVRank[j] / outDegree + (1 - alpha) * hVRank[j] / outDegree;
                    }
                }
            }

            for(int i = 0; i < numNodes; i++) {
                if(graph.outdegree(i) > 0) {
                    hVRank[i] = 0;
                    for(int j : graph.successorArray(i)) {
                        int inDegree = igraph.outdegree(j);
                        hVRank[i] += beta * aVRank[j] / inDegree + (1 - beta) * hVRank[j] / inDegree;
                    }
                }
            }

            for(int i = 0; i < numNodes; i++) {
                aVRank[i] = dampening * aVRank[i] + (1 - dampening) * iAVRank[i];
                hVRank[i] = dampening * hVRank[i] + (1 - dampening) * hVRank[i];
            }
        }
    }
}