AI人工智能领域中Open AI的社交网络分析

    import networkx as nx
    import openai
    import numpy as np
    from sklearn.decomposition import PCA
    
    def enhanced_node_embedding(graph, text_data, api_key):
    """
    使用OpenAI增强的节点嵌入算法
    :param graph: 网络图对象
    :param text_data: 节点相关文本数据
    :param api_key: OpenAI API密钥
    :return: 节点嵌入矩阵
    """
    openai.api_key = api_key
    
    # 传统图结构特征
    structural_embeddings = {}
    for node in graph.nodes():
        # 计算结构特征
        degree = graph.degree(node)
        clustering = nx.clustering(graph, node)
        centrality = nx.degree_centrality(graph)[node]
        structural_embeddings[node] = [degree, clustering, centrality]
    
    # OpenAI语义特征
    semantic_embeddings = {}
    for node, text in text_data.items():
        response = openai.Embedding.create(
            input=text,
            model="text-embedding-ada-002"
        )
        embedding = response['data'][0]['embedding']
        semantic_embeddings[node] = embedding
    
    # 特征融合
    combined_embeddings = {}
    for node in graph.nodes():
        struct_feat = structural_embeddings.get(node, [0,0,0])
        semantic_feat = semantic_embeddings.get(node, [0]*1536)
        combined = struct_feat + semantic_feat
        combined_embeddings[node] = combined
    
    # 降维处理
    nodes = list(graph.nodes())
    embedding_matrix = np.array([combined_embeddings[node] for node in nodes])
    pca = PCA(n_components=128)
    reduced_embeddings = pca.fit_transform(embedding_matrix)
    
    return {node: emb for node, emb in zip(nodes, reduced_embeddings)}
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/AHvZpJghFz2ntfNkMw5RQoI18Ybl.png)

    import community as community_louvain
    from datetime import datetime
    from collections import defaultdict
    
    def dynamic_community_detection(graph, historical_posts, time_windows, api_key):
    """
    动态社区检测算法
    :param graph: 基础图结构
    :param historical_posts: 按时间戳记录的历史发帖数据
    :param time_windows: 时间窗口划分
    :param api_key: OpenAI API密钥
    :return: 各时间段的社区划分
    """
    openai.api_key = api_key
    community_results = {}
    
    for i, window in enumerate(time_windows):
        start, end = window
        window_posts = defaultdict(list)
    
        # 收集时间窗口内的文本
        for timestamp, post in historical_posts:
            if start <= timestamp <= end:
                window_posts[post['author']].append(post['text'])
    
        # 使用OpenAI分析文本情感和主题
        author_topics = {}
        for author, texts in window_posts.items():
            combined_text = " ".join(texts)
            prompt = f"分析以下社交网络发帖内容的主要主题和情感倾向:\n\n{combined_text}\n\n请用3-5个关键词概括。"
    
            response = openai.ChatCompletion.create(
                model="gpt-4",
                messages=[{"role": "user", "content": prompt}],
                max_tokens=100
            )
            analysis = response.choices[0].message.content
            author_topics[author] = analysis
    
        # 构建增强的图结构
        enhanced_graph = graph.copy()
        for author, analysis in author_topics.items():
            if author in enhanced_graph:
                enhanced_graph.nodes[author]['topic'] = analysis
    
        # 社区检测
        partition = community_louvain.best_partition(enhanced_graph)
        community_results[f"window_{i}"] = partition
    
    return community_results
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/9VbUtKDcvOH6eJhw1ZTQysCgaXF5.png)

    import numpy as np
    from sklearn.ensemble import RandomForestClassifier
    
    def influence_prediction(graph, historical_cascades, api_key):
    """
    影响力传播预测算法
    :param graph: 社交网络图
    :param historical_cascades: 历史传播案例
    :param api_key: OpenAI API密钥
    :return: 训练好的预测模型
    """
    openai.api_key = api_key
    
    # 准备训练数据
    X = []
    y = []
    
    for cascade in historical_cascades:
        initiator = cascade['initiator']
        content = cascade['content']
        spread_nodes = cascade['spread_nodes']
    
        # 节点结构特征
        degree = graph.degree(initiator)
        betweenness = nx.betweenness_centrality(graph)[initiator]
        clustering = nx.clustering(graph, initiator)
    
        # 使用OpenAI分析内容特征
        prompt = f"评估以下社交网络内容的传播潜力:\n\n{content}\n\n请从传播性角度给出1-10的评分。"
    
        response = openai.ChatCompletion.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}],
            max_tokens=10
        )
        try:
            virality_score = float(response.choices[0].message.content.strip())
        except:
            virality_score = 5.0  # 默认值
    
        # 构建特征向量
        features = [degree, betweenness, clustering, virality_score]
        X.append(features)
    
        # 传播范围作为目标变量
        spread_size = len(spread_nodes)
        y.append(spread_size)
    
    # 训练预测模型
    model = RandomForestClassifier(n_estimators=100)
    model.fit(X, y)
    
    return model
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/M8lmfHJ4pgkFseK9dLr5tnI60QaN.png)

    # 创建conda环境
    conda create -n openai-sna python=3.9
    conda activate openai-sna
    
    # 安装核心库
    pip install networkx matplotlib numpy pandas scikit-learn openai python-louvain
    
    # 可选：安装PyTorch Geometric用于图神经网络
    pip install torch torch-geometric
    
    
    bash

    import networkx as nx
    import openai
    import pandas as pd
    from sklearn.cluster import KMeans
    import matplotlib.pyplot as plt
    
    class SocialNetworkAnalyzer:
    def __init__(self, api_key):
        self.api_key = api_key
        openai.api_key = api_key
        self.graph = nx.Graph()
    
    def load_network_data(self, nodes_file, edges_file):
        """加载网络数据"""
        # 读取节点数据
        nodes_df = pd.read_csv(nodes_file)
        for _, row in nodes_df.iterrows():
            self.graph.add_node(row['id'],
                              name=row['name'],
                              bio=row['bio'],
                              followers=row['followers'])
    
        # 读取边数据
        edges_df = pd.read_csv(edges_file)
        for _, row in edges_df.iterrows():
            self.graph.add_edge(row['source'], row['target'],
                              weight=row['weight'])
    
    def analyze_influence(self, content):
        """
        分析内容在社交网络中的潜在影响力
        :param content: 要分析的内容
        :return: 影响力预测结果
        """
        # 获取网络中心性指标
        degrees = nx.degree_centrality(self.graph)
        betweenness = nx.betweenness_centrality(self.graph)
        closeness = nx.closeness_centrality(self.graph)
    
        # 使用OpenAI分析内容特征
        content_analysis = self._get_content_analysis(content)
    
        # 评估每个节点的传播潜力
        influence_scores = {}
        for node in self.graph.nodes():
            # 综合结构特征
            struct_score = 0.3*degrees[node] + 0.4*betweenness[node] + 0.3*closeness[node]
    
            # 结合内容分析
            node_bio = self.graph.nodes[node]['bio']
            relevance = self._get_semantic_similarity(content, node_bio)
    
            # 最终影响力分数
            influence_scores[node] = struct_score * relevance
    
        return influence_scores
    
    def _get_content_analysis(self, text):
        """使用OpenAI分析内容特征"""
        prompt = f"""分析以下社交媒体内容的传播特性:
    
        {text}
    
        请评估:
        1. 情感极性(1-10)
        2. 争议性(1-10)
        3. 时效性(1-10)
        4. 主题热度(1-10)
        返回格式: 情感,争议,时效,热度
        """
    
        response = openai.ChatCompletion.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}],
            max_tokens=100
        )
    
        analysis = response.choices[0].message.content
        try:
            sentiment, controversy, timeliness, popularity = map(float, analysis.split(','))
            return {
                'sentiment': sentiment,
                'controversy': controversy,
                'timeliness': timeliness,
                'popularity': popularity
            }
        except:
            return {'sentiment': 5, 'controversy': 5, 'timeliness': 5, 'popularity': 5}
    
    def _get_semantic_similarity(self, text1, text2):
        """获取两段文本的语义相似度"""
        response = openai.Embedding.create(
            input=[text1, text2],
            model="text-embedding-ada-002"
        )
        emb1 = response['data'][0]['embedding']
        emb2 = response['data'][1]['embedding']
        return np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2))
    
    def visualize_communities(self):
        """可视化社区结构"""
        # 获取节点嵌入
        embeddings = self._get_node_embeddings()
    
        # 聚类分析
        kmeans = KMeans(n_clusters=5)
        clusters = kmeans.fit_predict(list(embeddings.values()))
    
        # 可视化
        pos = nx.spring_layout(self.graph)
        plt.figure(figsize=(12, 8))
        nx.draw_networkx_nodes(self.graph, pos, node_color=clusters, cmap=plt.cm.tab20)
        nx.draw_networkx_edges(self.graph, pos, alpha=0.1)
        plt.title("Social Network Community Structure")
        plt.show()
    
    def _get_node_embeddings(self):
        """获取节点嵌入表示"""
        embeddings = {}
        for node in self.graph.nodes():
            bio = self.graph.nodes[node]['bio']
            response = openai.Embedding.create(
                input=bio,
                model="text-embedding-ada-002"
            )
            embeddings[node] = response['data'][0]['embedding']
        return embeddings
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/KVm9hCMrabtALHeNoyJ80sj7OiuD.png)

    analyzer = SocialNetworkAnalyzer(api_key="your-openai-key")
    analyzer.load_network_data("nodes.csv", "edges.csv")
    
    # 分析内容影响力
    content = "Check out our new AI tool that revolutionizes social network analysis!"
    scores = analyzer.analyze_influence(content)
    
    # 可视化社区
    analyzer.visualize_communities()
    
    
    python