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| public class SocialNetworkAnalyzer {
private Graph socialNetwork;
private Map<Integer, String> userNames;
public SocialNetworkAnalyzer() {
this.socialNetwork = new Graph(1000); // 支持1000个用户
this.userNames = new HashMap<>();
}
/**
* 添加用户
*/
public void addUser(int userId, String userName) {
userNames.put(userId, userName);
}
/**
* 添加好友关系
*/
public void addFriendship(int user1, int user2) {
socialNetwork.addEdge(user1, user2);
}
/**
* 查找共同好友
*/
public List<String> findMutualFriends(int user1, int user2) {
Set<Integer> friends1 = new HashSet<>(socialNetwork.getNeighbors(user1));
Set<Integer> friends2 = new HashSet<>(socialNetwork.getNeighbors(user2));
friends1.retainAll(friends2); // 求交集
return friends1.stream()
.map(userNames::get)
.collect(Collectors.toList());
}
/**
* 计算社交距离（六度分隔理论）
*/
public int calculateSocialDistance(int user1, int user2) {
BFSTraversal bfs = new BFSTraversal();
Map<Integer, Integer> distances = bfs.shortestDistances(socialNetwork, user1);
return distances.getOrDefault(user2, -1);
}
/**
* 推荐可能认识的人（朋友的朋友）
*/
public List<String> recommendFriends(int userId, int maxRecommendations) {
BFSTraversal bfs = new BFSTraversal();
List<List<Integer>> levels = bfs.levelOrderTraversal(socialNetwork, userId);
List<String> recommendations = new ArrayList<>();
// 推荐距离为2的用户（朋友的朋友）
if (levels.size() > 2) {
List<Integer> friendsOfFriends = levels.get(2);
for (int i = 0; i < Math.min(friendsOfFriends.size(), maxRecommendations); i++) {
int recommendedUserId = friendsOfFriends.get(i);
recommendations.add(userNames.get(recommendedUserId));
}
}
return recommendations;
}
/**
* 检测社交圈子（连通组件）
*/
public List<List<String>> findSocialCircles() {
ConnectedComponents cc = new ConnectedComponents();
List<List<Integer>> components = cc.findConnectedComponents(socialNetwork);
return components.stream()
.map(component -> component.stream()
.map(userNames::get)
.collect(Collectors.toList()))
.collect(Collectors.toList());
}
/**
* 查找影响力最大的用户
*/
public String findMostInfluentialUser() {
int maxConnections = 0;
int mostInfluentialUser = -1;
for (int userId : userNames.keySet()) {
int connections = socialNetwork.getNeighbors(userId).size();
if (connections > maxConnections) {
maxConnections = connections;
mostInfluentialUser = userId;
}
}
return userNames.get(mostInfluentialUser);
}
/**
* 分析信息传播路径
*/
public Map<String, Integer> analyzeInformationSpread(int sourceUser, int maxLevels) {
BFSTraversal bfs = new BFSTraversal();
List<List<Integer>> levels = bfs.levelOrderTraversal(socialNetwork, sourceUser);
Map<String, Integer> spreadAnalysis = new HashMap<>();
for (int level = 0; level < Math.min(levels.size(), maxLevels); level++) {
for (int userId : levels.get(level)) {
spreadAnalysis.put(userNames.get(userId), level);
}
}
return spreadAnalysis;
}
/**
* 完整的演示方法
*/
public void demonstrateFeatures() {
// 添加示例用户
addUser(1, "Alice");
addUser(2, "Bob");
addUser(3, "Charlie");
addUser(4, "David");
addUser(5, "Eve");
addUser(6, "Frank");
// 添加好友关系
addFriendship(1, 2); // Alice - Bob
addFriendship(1, 3); // Alice - Charlie
addFriendship(2, 4); // Bob - David
addFriendship(3, 5); // Charlie - Eve
addFriendship(4, 6); // David - Frank
System.out.println("=== 社交网络分析演示 ===");
// 查找共同好友
List<String> mutualFriends = findMutualFriends(1, 2);
System.out.println("Alice和Bob的共同好友: " + mutualFriends);
// 计算社交距离
int distance = calculateSocialDistance(1, 6);
System.out.println("Alice到Frank的社交距离: " + distance);
// 推荐朋友
List<String> recommendations = recommendFriends(1, 3);
System.out.println("为Alice推荐的朋友: " + recommendations);
// 查找影响力最大的用户
String influencer = findMostInfluentialUser();
System.out.println("影响力最大的用户: " + influencer);
// 分析信息传播
Map<String, Integer> spreadAnalysis = analyzeInformationSpread(1, 3);
System.out.println("从Alice开始的信息传播分析: " + spreadAnalysis);
}
}
|