[{"data":1,"prerenderedAt":741},["ShallowReactive",2],{"navigation_docs":3,"-data-analysis-social-network-analysis-sna":352,"-data-analysis-social-network-analysis-sna-surround":736},[4,54,83,116,125,161,179,200],{"title":5,"path":6,"stem":7,"children":8},"Ai","/ai","ai",[9,12,42,46,50],{"title":10,"path":6,"stem":11},"AI 技術探索","ai/index",{"title":13,"path":14,"stem":15,"children":16,"page":41},"Rag","/ai/rag","ai/RAG",[17,21,25,29,33,37],{"title":18,"path":19,"stem":20},"BM25 演算法深入解析：從 TF-IDF 到現代搜尋引擎的核心技術","/ai/rag/bm25-deep-dive-from-tf-idf-to-rag","ai/RAG/bm25-deep-dive-from-tf-idf-to-rag",{"title":22,"path":23,"stem":24},"Dense Retrieval（向量檢索）深入解析","/ai/rag/dense-retrieval-deep-dive","ai/RAG/dense-retrieval-deep-dive",{"title":26,"path":27,"stem":28},"RAG（Retrieval-Augmented Generation）原理與實作入門","/ai/rag/rag-intro-principles-and-implementation","ai/RAG/rag-intro-principles-and-implementation",{"title":30,"path":31,"stem":32},"RAG Retrieval Strategy 深入解析","/ai/rag/rag-retrieval-strategy-deep-dive","ai/RAG/rag-retrieval-strategy-deep-dive",{"title":34,"path":35,"stem":36},"Reciprocal Rank Fusion（RRF）教學：打造更穩定的檢索融合策略","/ai/rag/rrf-reciprocal-rank-fusion-guide","ai/RAG/rrf-reciprocal-rank-fusion-guide",{"title":38,"path":39,"stem":40},"TF-IDF（Term Frequency–Inverse Document Frequency）深入解析","/ai/rag/tf-idf-deep-dive","ai/RAG/tf-idf-deep-dive",false,{"title":43,"path":44,"stem":45},"Docus AI 功能完整實作指南","/ai/docus-ai-implementation","ai/docus-ai-implementation",{"title":47,"path":48,"stem":49},"llms.txt 是什麼？在 Nuxt 中控制 AI 與搜尋引擎爬蟲的新方式","/ai/llms-txt","ai/llms-txt",{"title":51,"path":52,"stem":53},"MCP（Model Context Protocol）是什麼？用「AI 的 USB-C」理解模型如何安全連接工具與資料","/ai/mcp_concept","ai/mcp_concept",{"title":55,"path":56,"stem":57,"children":58,"page":41},"Data Analysis","/data-analysis","data-analysis",[59,63,67,71,75,79],{"title":60,"path":61,"stem":62},"Exploratory Data Analysis（EDA）資料探索分析入門","/data-analysis/exploratory-data-analysis-eda","data-analysis/exploratory-data-analysis-eda",{"title":64,"path":65,"stem":66},"PR-AUC 深入解析：不平衡分類問題的重要評估指標","/data-analysis/pr-auc-for-imbalanced-classification","data-analysis/pr-auc-for-imbalanced-classification",{"title":68,"path":69,"stem":70},"ROC-AUC 深入解析：理解分類模型的判斷能力","/data-analysis/roc-auc-complete-guide","data-analysis/roc-auc-complete-guide",{"title":72,"path":73,"stem":74},"SHAP（SHapley Additive exPlanations）模型解釋方法深入解析","/data-analysis/shap-model-interpretation-complete-guide","data-analysis/shap-model-interpretation-complete-guide",{"title":76,"path":77,"stem":78},"Social Network Analysis（SNA）深入解析","/data-analysis/social-network-analysis-sna","data-analysis/social-network-analysis-sna",{"title":80,"path":81,"stem":82},"Time-based Split：為什麼時間序列資料不能使用 Random Split？","/data-analysis/time-based-split-vs-random-split","data-analysis/time-based-split-vs-random-split",{"title":84,"path":85,"stem":86,"children":87,"page":41},"MachineLearning","/machine_learning","machine_learning",[88,92,96,100,104,108,112],{"title":89,"path":90,"stem":91},"Confusion Matrix（混淆矩陣）完整解析","/machine_learning/confusion-matrix","machine_learning/confusion-matrix",{"title":93,"path":94,"stem":95},"Decision Tree 模型完整教學：從原理到 Python 實作","/machine_learning/decision-tree-complete-guide","machine_learning/decision-tree-complete-guide",{"title":97,"path":98,"stem":99},"為什麼 Decision Tree 在 Tabular Data 上常常勝過 Deep Learning？","/machine_learning/decision-tree-vs-deep-learning-on-tabular-data","machine_learning/decision-tree-vs-deep-learning-on-tabular-data",{"title":101,"path":102,"stem":103},"LightGBM 模型入門：理解高效能的 Gradient Boosting 演算法","/machine_learning/lightgbm-intro-for-tabular-data","machine_learning/lightgbm-intro-for-tabular-data",{"title":105,"path":106,"stem":107},"Logistic Regression（Logit 回歸）完整教學","/machine_learning/logistic-regression-complete-guide","machine_learning/logistic-regression-complete-guide",{"title":109,"path":110,"stem":111},"為什麼模型訓練需要 Train / Validation / Test Dataset？","/machine_learning/train-validation-test-dataset","machine_learning/train-validation-test-dataset",{"title":113,"path":114,"stem":115},"XGBoost 原理與實務教學","/machine_learning/xgboost-complete-guide","machine_learning/xgboost-complete-guide",{"title":117,"path":118,"stem":119,"children":120,"page":41},"Network","/network","network",[121],{"title":122,"path":123,"stem":124},"DNS 設定入門：CNAME vs A Record 完整解析","/network/dns-cname-vs-a-record-guide","network/dns-cname-vs-a-record-guide",{"title":126,"path":127,"stem":128,"children":129},"Nuxt","/nuxt","nuxt",[130,133,137,141,145,149,153,157],{"title":131,"path":127,"stem":132},"Nuxt 實戰指南","nuxt/index",{"title":134,"path":135,"stem":136},"Abstraction Layer 與 Adapter 的差別，一次搞懂設計角色","/nuxt/adapter_abstraction_layer","nuxt/adapter_abstraction_layer",{"title":138,"path":139,"stem":140},"Nuxt 4 全面理解 — 專案檔案結構解析","/nuxt/directory_structure","nuxt/directory_structure",{"title":142,"path":143,"stem":144},"Nuxt 前端 build 流程完整解析","/nuxt/frontend_build","nuxt/frontend_build",{"title":146,"path":147,"stem":148},"Nitro 是什麼？從 Nuxt 專案到 Server 與 Edge 的關鍵引擎","/nuxt/nitro","nuxt/nitro",{"title":150,"path":151,"stem":152},"深入理解 Nuxt 的 .nuxt 與 .output：為什麼部署時一定要分清楚？","/nuxt/nuxt_and_output","nuxt/nuxt_and_output",{"title":154,"path":155,"stem":156},"Nuxt 中 dev、build、preview 的差異一次搞懂","/nuxt/nuxt_dev_build_preview","nuxt/nuxt_dev_build_preview",{"title":158,"path":159,"stem":160},"Nuxt Image vs \u003Cimg>：為什麼 Nuxt 3 專案幾乎都該用 \u003CNuxtImg>？","/nuxt/nuxt_image","nuxt/nuxt_image",{"title":162,"path":163,"stem":164,"children":165},"Python 程式開發","/python","python/index",[166,167,171,175],{"title":162,"path":163,"stem":164},{"title":168,"path":169,"stem":170},"如何在 FastAPI 中調用記憶體 Buffer 回傳圖片 (附實例)","/python/memory_buffer","python/memory_buffer",{"title":172,"path":173,"stem":174},"Python 單元測試教學：如何為你的程式撰寫 Test","/python/python-unit-testing-unittest-pytest","python/python-unit-testing-unittest-pytest",{"title":176,"path":177,"stem":178},"如何使用 uv 取代 pip：改善 Python 專案的開發流程","/python/uv","python/uv",{"title":180,"path":181,"stem":182,"children":183,"page":41},"Statistic","/statistic","statistic",[184,188,192,196],{"title":185,"path":186,"stem":187},"卡方檢定（Chi-Square Test）入門教學","/statistic/chi-square-test-intro","statistic/chi-square-test-intro",{"title":189,"path":190,"stem":191},"常見抽樣方法（Sampling Methods）教學：Stratified、Quota、Convenience、Systematic、Simple Random","/statistic/common-sampling-methods-guide","statistic/common-sampling-methods-guide",{"title":193,"path":194,"stem":195},"Cramér’s V 指標介紹：如何衡量兩個類別變數之間的關聯","/statistic/cramers-v-for-categorical-association","statistic/cramers-v-for-categorical-association",{"title":197,"path":198,"stem":199},"Mann–Whitney U 與 Kolmogorov–Smirnov（KS）檢定：非參數統計檢定的入門指南","/statistic/mann-whitney-u-and-ks-test-intro","statistic/mann-whitney-u-and-ks-test-intro",{"title":201,"path":202,"stem":203,"children":204},"WebDev","/web_dev","web_dev",[205,208,234,248,282,312,334],{"title":206,"path":202,"stem":207},"Web 前端開發","web_dev/index",{"title":209,"path":210,"stem":211,"children":212},"瀏覽器與渲染","/web_dev/browser","web_dev/browser/index",[213,214,218,222,226,230],{"title":209,"path":210,"stem":211},{"title":215,"path":216,"stem":217},"瀏覽器儲存空間完整解析：Cookie、localStorage、IndexedDB 到 Cache Storage","/web_dev/browser/browser-storage-comprehensive-guide","web_dev/browser/browser-storage-comprehensive-guide",{"title":219,"path":220,"stem":221},"DOM (Document Object Model) 深入解析","/web_dev/browser/dom","web_dev/browser/dom",{"title":223,"path":224,"stem":225},"Service Worker Request Flow 深入解析","/web_dev/browser/service-worker-request-flow","web_dev/browser/service-worker-request-flow",{"title":227,"path":228,"stem":229},"CSR、SSR 與 SSG 是什麼？前端渲染策略完整比較","/web_dev/browser/ssr_csr_ssg","web_dev/browser/ssr_csr_ssg",{"title":231,"path":232,"stem":233},"Virtual DOM 深入解析：為什麼它能優化前端效能？","/web_dev/browser/virtual_dom","web_dev/browser/virtual_dom",{"title":235,"path":236,"stem":237,"children":238},"圖形技術","/web_dev/graphics","web_dev/graphics/index",[239,240,244],{"title":235,"path":236,"stem":237},{"title":241,"path":242,"stem":243},"為什麼 Three.js 專案幾乎都選擇 CSR？從 SSR 問題談起","/web_dev/graphics/threejs_csr","web_dev/graphics/threejs_csr",{"title":245,"path":246,"stem":247},"WebGL 是什麼？為什麼前端 3D 幾乎都靠它？","/web_dev/graphics/webgl","web_dev/graphics/webGL",{"title":249,"path":250,"stem":251,"children":252},"架構與配置","/web_dev/infrastructure","web_dev/infrastructure/index",[253,254,258,262,266,270,274,278],{"title":249,"path":250,"stem":251},{"title":255,"path":256,"stem":257},"Zeabur + K3s + Nuxt 部署架構完整解析","/web_dev/infrastructure/k3s-zeabur","web_dev/infrastructure/K3s-zeabur",{"title":259,"path":260,"stem":261},"ECS vs Docker vs Kubernetes：從部署堆疊理解三層架構","/web_dev/infrastructure/ecs-docker-kubernetes-stack","web_dev/infrastructure/ecs-docker-kubernetes-stack",{"title":263,"path":264,"stem":265},"ECS vs VPS 是什麼？從部署網站的角度一次搞懂差別","/web_dev/infrastructure/ecs-vs-vps","web_dev/infrastructure/ecs-vs-vps",{"title":267,"path":268,"stem":269},"Zeabur 內網服務連線完整入門指南","/web_dev/infrastructure/k3s-internal-networking","web_dev/infrastructure/k3s-internal-networking",{"title":271,"path":272,"stem":273},"使用 Microservices 架構設計系統的優勢解析","/web_dev/infrastructure/microservices-architecture-advantages","web_dev/infrastructure/microservices-architecture-advantages",{"title":275,"path":276,"stem":277},"Nginx 入門教學：從反向代理到與 Kubernetes 的架構比較","/web_dev/infrastructure/nginx-intro","web_dev/infrastructure/nginx-intro",{"title":279,"path":280,"stem":281},"YAML 配置是什麼？為什麼現代開發都在用它","/web_dev/infrastructure/yaml-configuration","web_dev/infrastructure/yaml-configuration",{"title":283,"path":284,"stem":285,"children":286},"網絡與通訊","/web_dev/network","web_dev/network/index",[287,288,292,296,300,304,308],{"title":283,"path":284,"stem":285},{"title":289,"path":290,"stem":291},"HTTP Request 結構完整解析：從 Request Line 到 Header 一次看懂","/web_dev/network/http-request-structure","web_dev/network/http-request-structure",{"title":293,"path":294,"stem":295},"OSI 模型（Open Systems Interconnection Model）完整解析","/web_dev/network/osi-model","web_dev/network/osi-model",{"title":297,"path":298,"stem":299},"RESTful API 設計原則與實務解析","/web_dev/network/restful-api","web_dev/network/restful-api",{"title":301,"path":302,"stem":303},"RESTful API 五大設計原則深入解析","/web_dev/network/restful-api-principles","web_dev/network/restful-api-principles",{"title":305,"path":306,"stem":307},"Server-Sent Events (SSE) 深入解析與實作教學","/web_dev/network/sse-introduction","web_dev/network/sse-introduction",{"title":309,"path":310,"stem":311},"WebSocket 入門教學：從概念到 Node.js 實作","/web_dev/network/websocket-introduction","web_dev/network/websocket-introduction",{"title":313,"path":314,"stem":315,"children":316},"認證與安全","/web_dev/security","web_dev/security/index",[317,318,322,326,330],{"title":313,"path":314,"stem":315},{"title":319,"path":320,"stem":321},"使用 Cookie 與 Session 建立使用者驗證（完整新手教學）","/web_dev/security/cookie-session-authentication","web_dev/security/cookie-session-authentication",{"title":323,"path":324,"stem":325},"JWT 驗證機制完整解析：從登入流程到實務應用","/web_dev/security/jwt-authentication","web_dev/security/jwt-authentication",{"title":327,"path":328,"stem":329},"Secret Key 簽名是什麼？從零理解資料簽名的本質","/web_dev/security/secret-key-signing","web_dev/security/secret-key-signing",{"title":331,"path":332,"stem":333},"SSH（Secure Shell）是什麼？從遠端登入到安全通道的核心概念","/web_dev/security/ssh","web_dev/security/ssh",{"title":335,"path":336,"stem":337,"children":338},"SEO 與規範","/web_dev/seo","web_dev/seo/index",[339,340,344,348],{"title":335,"path":336,"stem":337},{"title":341,"path":342,"stem":343},"Canonical URL 是什麼？用生活化方式搞懂前端 SEO 的基本保命符","/web_dev/seo/canonical_link","web_dev/seo/canonical_link",{"title":345,"path":346,"stem":347},"robots.txt 是什麼？SEO 的第一道守門員","/web_dev/seo/robot_txt","web_dev/seo/robot_txt",{"title":349,"path":350,"stem":351},"Nuxt SEO 中的 Meta 與 SEO 工具是如何運作的？","/web_dev/seo/seo","web_dev/seo/seo",{"id":353,"title":76,"body":354,"description":723,"extension":724,"links":725,"meta":726,"navigation":733,"path":77,"seo":734,"stem":78,"__hash__":735},"docs/data-analysis/social-network-analysis-sna.md",{"type":355,"value":356,"toc":707},"minimark",[357,362,366,369,382,385,389,392,406,409,412,426,429,440,446,448,452,455,460,463,469,474,477,483,493,495,499,502,507,510,513,516,530,533,541,546,548,552,555,558,563,565,569,572,577,579,583,589,596,607,610,621,630,632,636,639,646,652,658,667,669,672,675,678,680,683],[358,359,361],"h2",{"id":360},"什麼是-social-network-analysis","什麼是 Social Network Analysis？",[363,364,365],"p",{},"在許多真實世界的系統中，資料往往不是孤立存在的，而是透過各種關係彼此連結。例如社群媒體中的朋友關係、金融交易中的資金流動、學術論文的引用關係，甚至是網站之間的連結。**Social Network Analysis（SNA，社會網路分析）**正是一種用來研究「關係結構」的分析方法。",[363,367,368],{},"與傳統資料分析不同，SNA 的重點不只是在個體本身的屬性，而是關注**個體之間的連結（relationships）**以及整體網路結構如何影響系統的行為。透過分析這些關係，我們可以理解資訊如何傳播、哪些節點具有影響力，以及整個網路是否存在群體結構。",[363,370,371,372,376,377,381],{},"因此，SNA 在許多領域都有重要應用，例如社群媒體分析、金融詐欺偵測、推薦系統、知識傳播研究，以及疫情傳播模型等。它通常建立在 ",[373,374,375],"strong",{},"Graph Theory（圖論）"," 的基礎上，將系統中的實體與關係轉換成可以計算與分析的網路結構 ",[378,379,380],"span",{},"1","。",[383,384],"hr",{},[358,386,388],{"id":387},"sna-的基本結構nodes-與-edges","SNA 的基本結構：Nodes 與 Edges",[363,390,391],{},"在社會網路分析中，任何網路都可以用兩個基本元素來描述：",[393,394,395,401],"ul",{},[396,397,398],"li",{},[373,399,400],{},"Node（節點）",[396,402,403],{},[373,404,405],{},"Edge（邊）",[363,407,408],{},"節點代表網路中的個體，例如人、帳戶、公司或網站；而邊則代表節點之間的關係，例如朋友關係、交易行為或訊息傳遞。",[363,410,411],{},"舉例來說，在一個金融交易網路中：",[393,413,414,420],{},[396,415,416,417],{},"每個銀行帳戶可以視為一個 ",[373,418,419],{},"node",[396,421,422,423],{},"每一筆資金轉帳則是一條 ",[373,424,425],{},"edge",[363,427,428],{},"用圖形方式表示時，網路通常會呈現如下結構：",[430,431,436],"pre",{"className":432,"code":434,"language":435},[433],"language-text","A ---- B\n|      |\n|      |\nC ---- D\n","text",[437,438,434],"code",{"__ignoreMap":439},"",[363,441,442,443,381],{},"在這個例子中，A、B、C、D 是節點，而連接它們的線條就是邊。透過這種方式，我們可以將複雜的關係資料轉換成圖形結構，進一步利用數學與演算法來分析整體網路特性 ",[378,444,445],{},"2",[383,447],{},[358,449,451],{"id":450},"directed-network-與-undirected-network","Directed Network 與 Undirected Network",[363,453,454],{},"根據關係是否具有方向性，網路通常可以分為兩種類型：",[363,456,457],{},[373,458,459],{},"Undirected Network（無向網路）",[363,461,462],{},"在無向網路中，關係是雙向的。例如 Facebook 的好友關係，如果 A 是 B 的朋友，通常 B 也是 A 的朋友，因此這種連結沒有方向。",[430,464,467],{"className":465,"code":466,"language":435},[433],"A ---- B\n",[437,468,466],{"__ignoreMap":439},[363,470,471],{},[373,472,473],{},"Directed Network（有向網路）",[363,475,476],{},"在有向網路中，關係具有方向。例如 Twitter 的追蹤關係，A 可以追蹤 B，但 B 不一定追蹤 A，因此邊會具有方向。",[430,478,481],{"className":479,"code":480,"language":435},[433],"A ---> B\n",[437,482,480],{"__ignoreMap":439},[363,484,485,486,489,490,381],{},"在金融交易或資訊傳播的研究中，通常會使用 ",[373,487,488],{},"directed network","，因為資金流動與訊息傳播本身就具有方向性 ",[378,491,492],{},"3",[383,494],{},[358,496,498],{"id":497},"常見的網路指標network-metrics","常見的網路指標（Network Metrics）",[363,500,501],{},"當資料被轉換為網路結構後，我們就可以利用各種指標來描述節點在網路中的角色與重要性。以下是幾個最常見的網路指標。",[503,504,506],"h3",{"id":505},"degree-centrality","Degree Centrality",[363,508,509],{},"**Degree Centrality（度中心性）**是最直觀的網路指標之一，它代表一個節點與多少其他節點相連。",[363,511,512],{},"在社群網路中，degree 可以理解為一個人擁有多少朋友；在交易網路中，則可能代表一個帳戶與多少帳戶發生過交易。",[363,514,515],{},"如果在有向網路中，degree 會進一步分成：",[393,517,518,524],{},[396,519,520,523],{},[373,521,522],{},"In-degree","：收到多少連結",[396,525,526,529],{},[373,527,528],{},"Out-degree","：發出多少連結",[363,531,532],{},"例如在 Twitter 中：",[393,534,535,538],{},[396,536,537],{},"In-degree = 有多少人追蹤你",[396,539,540],{},"Out-degree = 你追蹤了多少人",[363,542,543,544,381],{},"Degree Centrality 常用來找出網路中最活躍或最具影響力的節點 ",[378,545,380],{},[383,547],{},[503,549,551],{"id":550},"betweenness-centrality","Betweenness Centrality",[363,553,554],{},"**Betweenness Centrality（中介中心性）**用來衡量一個節點在網路中扮演「橋樑」的程度。",[363,556,557],{},"如果許多節點之間的最短路徑都需要經過某一個節點，代表這個節點在資訊傳播或資源流動上具有關鍵地位。這種節點通常可以控制資訊的流動，因此在社群網路或金融網路中具有重要意義。",[363,559,560,561,381],{},"例如在企業組織中，一些跨部門的協調者往往具有較高的 betweenness centrality，因為許多資訊需要透過他們在不同團隊之間流動 ",[378,562,445],{},[383,564],{},[503,566,568],{"id":567},"closeness-centrality","Closeness Centrality",[363,570,571],{},"**Closeness Centrality（接近中心性）**衡量一個節點與網路中所有其他節點的距離。",[363,573,574,575,381],{},"如果一個節點到其他節點的平均距離較短，代表它可以更快接觸到整個網路中的資訊。這種節點通常在資訊傳播過程中具有優勢，例如在病毒傳播或消息擴散研究中，closeness centrality 經常被用來找出最可能快速擴散訊息的節點 ",[378,576,492],{},[383,578],{},[358,580,582],{"id":581},"社群結構community-detection","社群結構（Community Detection）",[363,584,585,586,381],{},"在大型網路中，節點通常會形成不同的群體結構，這些群體內部連結密集，但與其他群體之間的連結較少。這種現象稱為 ",[373,587,588],{},"community structure",[363,590,591,592,595],{},"透過 ",[373,593,594],{},"Community Detection（社群偵測）"," 演算法，我們可以將整個網路分割成多個子群體。例如：",[393,597,598,601,604],{},[396,599,600],{},"社群媒體中的興趣社群",[396,602,603],{},"金融交易中的洗錢集團",[396,605,606],{},"學術論文中的研究領域群集",[363,608,609],{},"常見的社群偵測方法包括：",[393,611,612,615,618],{},[396,613,614],{},"Louvain Algorithm",[396,616,617],{},"Girvan–Newman Algorithm",[396,619,620],{},"Label Propagation",[363,622,623,624,627,628,381],{},"這些方法通常會透過最大化 ",[373,625,626],{},"modularity（模組度）"," 來找出最合理的社群分割方式 ",[378,629,445],{},[383,631],{},[358,633,635],{"id":634},"sna-的實際應用","SNA 的實際應用",[363,637,638],{},"社會網路分析在許多領域都有廣泛應用。",[363,640,641,642,645],{},"在 ",[373,643,644],{},"社群媒體分析"," 中，SNA 可以用來找出具有影響力的用戶，分析資訊如何在網路中傳播，並了解社群結構。",[363,647,641,648,651],{},[373,649,650],{},"金融詐欺偵測"," 中，銀行常會建立交易網路，透過分析帳戶之間的資金流動來找出可疑的交易模式。例如某些帳戶可能扮演資金中轉的角色，具有異常高的 betweenness centrality，這可能是洗錢活動的指標。",[363,653,641,654,657],{},[373,655,656],{},"推薦系統"," 中，SNA 也可以用來分析使用者之間的關係，透過網路結構來預測可能感興趣的商品或內容。",[363,659,660,661,664,665,381],{},"此外，在 ",[373,662,663],{},"疫情傳播研究"," 中，SNA 可以模擬病毒在社會網路中的傳播方式，並找出最有效的防疫策略，例如優先接種疫苗的群體 ",[378,666,492],{},[383,668],{},[358,670,671],{"id":671},"結論",[363,673,674],{},"Social Network Analysis 是一種專門研究「關係結構」的資料分析方法。透過將資料轉換為節點與邊所構成的網路，我們可以分析節點的重要性、資訊傳播路徑以及群體結構。這些分析不僅能幫助我們理解複雜系統的運作方式，也在社群媒體、金融風控、推薦系統與公共衛生等領域中發揮重要作用。",[363,676,677],{},"隨著資料規模持續成長，以及圖資料庫與分散式計算技術的發展，Social Network Analysis 也逐漸成為資料科學與人工智慧領域中的重要研究方向。",[383,679],{},[358,681,682],{"id":682},"參考資料",[684,685,686,694,701],"ol",{},[396,687,688,689,693],{},"Wasserman, S., & Faust, K. (1994). ",[690,691,692],"em",{},"Social Network Analysis: Methods and Applications",". Cambridge University Press.",[396,695,696,697,700],{},"Newman, M. (2010). ",[690,698,699],{},"Networks: An Introduction",". Oxford University Press.",[396,702,703,704,693],{},"Easley, D., & Kleinberg, J. (2010). ",[690,705,706],{},"Networks, Crowds, and Markets: Reasoning About a Highly Connected World",{"title":439,"searchDepth":708,"depth":708,"links":709},2,[710,711,712,713,719,720,721,722],{"id":360,"depth":708,"text":361},{"id":387,"depth":708,"text":388},{"id":450,"depth":708,"text":451},{"id":497,"depth":708,"text":498,"children":714},[715,717,718],{"id":505,"depth":716,"text":506},3,{"id":550,"depth":716,"text":551},{"id":567,"depth":716,"text":568},{"id":581,"depth":708,"text":582},{"id":634,"depth":708,"text":635},{"id":671,"depth":708,"text":671},{"id":682,"depth":708,"text":682},"介紹社會網路分析（Social Network Analysis, SNA）的基本概念、常見指標與應用場景，理解如何透過網路結構分析人際關係、資訊傳播與交易網路。","md",null,{"tags":727,"category":57,"date":732},[728,729,730,731],"social network analysis","graph","data science","network analysis","2026-03-07",true,{"title":76,"description":723},"at7wnPlMG5Zo1-NJZd6pmHU4xb4rt_ZyI5g5RsmyiR0",[737,739],{"title":72,"path":73,"stem":74,"description":738,"children":-1},"介紹 SHAP 指標的原理、 Shapley value 的概念，以及如何在機器學習模型中解釋特徵對預測結果的影響。",{"title":80,"path":81,"stem":82,"description":740,"children":-1},"介紹 Time-based Split 的概念、為什麼時間資料不能隨機切分，以及如何避免未來資料洩漏（Future Data Leakage）。",1776690841202]