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This paper introduces a Social Network Forensic Analysis model that employs network representation learning to identify and analyze key figures within criminal networks, including leadership structures. The model incorporates traditional web forensics and community algorithms, utilizing concepts such as centrality and similarity measures and integrating the Deepwalk, Line, and Node2vec algorithms to map criminal networks into vector spaces. This maintains node features and structural information that are crucial for the relational analysis. The model refines node relationships through modified random walk sampling, using BFS and DFS, and employs a Continuous Bag-of-Words with Hierarchical Softmax for node vectorization, optimizing the value distribution via the Huffman tree. Hierarchical clustering and distance measures (cosine and Euclidean) were used to identify the key nodes and establish a hierarchy of influence. The findings demonstrate the effectiveness of the model in accurately vectorizing nodes, enhancing inter-node relationship precision, and optimizing clustering, thereby advancing the tools for combating complex criminal networks.<\/jats:p>","DOI":"10.3390\/e26070579","type":"journal-article","created":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T11:30:02Z","timestamp":1720438202000},"page":"579","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Social Network Forensics Analysis Model Based on Network Representation Learning"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9254-6040","authenticated-orcid":false,"given":"Kuo","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China"},{"name":"Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics, Jinan University, Zhuhai 519070, China"},{"name":"Institute of Physical Internet, Jinan University, Zhuhai 519070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huajian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qifu","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Lai","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yike","family":"Nie","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongfei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics, Jinan University, Zhuhai 519070, China"},{"name":"Institute of Physical Internet, Jinan University, Zhuhai 519070, China"},{"name":"School of Management, Jinan University, Guangzhou 510632, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"026111","DOI":"10.1103\/PhysRevE.73.026111","article-title":"Traffic dynamics based on local routing protocol on a scale-free network","volume":"73","author":"Wang","year":"2006","journal-title":"Phys. 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