Please wait a minute...
文章检索
复杂系统与复杂性科学  2025, Vol. 22 Issue (2): 105-112    DOI: 10.13306/j.1672-3813.2025.02.013
  复杂网络 本期目录 | 过刊浏览 | 高级检索 |
基于异质模体特征的社交网络用户性别识别
向宇平, 许小可
大连民族大学信息与通信工程学院,辽宁 大连 116600
Gender Recognition of Social Network Users Based on Heterogeneous Motif Features
XIANG Yuping, XU Xiaoke
College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China
全文: PDF(2269 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 用户性别是用户画像的核心之一,精确识别用户性别的现有方法主要依赖用户公开属性,较少考虑网络结构信息。本研究基于模体理论融合性别信息,将同质模体细分成异质模体,提出一种基于异质模体特征的性别识别方法,提取更加细节的局部信息来区分不同性别的用户。所提方法,相较于目前流行的网络嵌入类方法,Accuracy指标提高了1.3%到14.2%,AUC指标提高了2.7%到15.8%,且在不同比例训练集上性能稳定。异质模体方法可应用于社交用户的身份检测,有助于挖掘社交网络用户属性特征。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
向宇平
许小可
关键词 社交网络异质模体性别识别社交关系    
Abstract:User gender is one of the core aspects of user profiling, and existing methods for accurately identifying user gender mainly rely on user public attributes, with less consideration given to network structure information. This study integrates gender information based on motif theory, subdivides homogeneous motifs into heterogeneous motifs, and proposes a gender recognition method based on heterogeneous motif features, extracting more detailed local information to distinguish users of different genders. Compared to the current popular network embedding methods, the method proposed in this article has improved the Accuracy index by 2.8% to 14.2%, and the AUC index by 2.7% to 15.8%, with stable performance on different proportion training sets. The heteromorphic method can be applied to the identity detection of social users, which helps to conduct in-depth research on the structural characteristics of social networks.
Key wordssocial network    heterogeneous motif    gender identification    social connections
收稿日期: 2023-07-06      出版日期: 2025-06-03
ZTFLH:  TB3  
  N94  
基金资助:国家自然科学基金(62173065)
通讯作者: 许小可(1979),男,辽宁庄河人,博士,教授,主要研究方向为网络科学和社交网络大数据。   
作者简介: 向宇平(1999),男,四川成都人,硕士研究生,主要研究方向为社交网络上的结构与属性特征。
引用本文:   
向宇平, 许小可. 基于异质模体特征的社交网络用户性别识别[J]. 复杂系统与复杂性科学, 2025, 22(2): 105-112.
XIANG Yuping, XU Xiaoke. Gender Recognition of Social Network Users Based on Heterogeneous Motif Features[J]. Complex Systems and Complexity Science, 2025, 22(2): 105-112.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2025.02.013      或      https://fzkx.qdu.edu.cn/CN/Y2025/V22/I2/105
[1] HE Z, CAI Z, YU J. Latent-data privacy preserving with customized data utility for social network data[J]. IEEE Transactions on Vehicular Technology, 2018, 67(1): 665673.
[2] SLOKOM M, HANJALICA, LARSON M. Towards user-oriented privacy for recommender system data: a personalization-based approach to gender obfuscation for user profiles[J]. Information Processing & Management, 2021, 58(6): 102722.
[3] JIA J, WANG B, ZHANG L, et al. AttriInfer: inferring user attributes in online social networks using markov random fields[DB/OL].[20221220]. https://dl.acm.org/doi/abs/10.1145/3038912.3052695.
[4] BURGER J D, HENDERSON J, KIM G, et al. Discriminating gender on twitter[C]//Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh: Association for Computational Linguistics, 2011: 13011309.
[5] ALOWIBDI J S, BUY U A, YU P. Language independent gender classification on Twitter[C]//International Conference on Advances In Social Networks Analysis and Mining, Niagara Falls: IEEE, 2013: 739743.
[6] KOSINSKI M, STILLWELL D, GRAEPEL T. Private traits and attributes are predictable from digital records of human behavior[J]. Proceedings of the National Academy of Sciences, 2013, 110(15): 58025805.
[7] HE J, CHU W W, LIU Z V. Inferring privacy information from social networks[C]]//International Conference on Intelligence and Security Informatics, Berlin, Heidelberg: Springer, 2006: 154165.
[8] XU W H, ZHOU X, LI L. Inferring privacy information via social relations[C]]//2008 IEEE 24th International Conference on Data Engineering Workshops, Cancun, Mexico: IEEE, 2008: 525530.
[9] ZHELEVA E, GETOOR L. To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles[C]//Proceedings of the 18th International Conference on World Wide Web, New York, NY, USA: ACM, 2009: 531540.
[10] PEROZZI B, AL-RFOU R, SKIENA S. Deepwalk: Online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference On Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2014: 701-710.
[11] TANG J, QU M, WANG M, et al. Large scale information network embedding[C]//Proceedings of the 24th International World Wide Web Conference, New York, NY, USA: ACM, 2015: 10671077.
[12] GROVER A, LESKOVEC J. Node2Vec: Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA: ACM,2016: 855864.
[13] 艾春玲, 何敏, 吕亮,等. 基于综合游走策略的边嵌入链路预测算法[J]. 云南大学学报(自然科学版), 2023, 45(1): 2937.
AI C L, HE M, LV L, et al. Edge embedding link prediction algorithm based on comprehensive walking strategy[J]. Journal of Yunnan University(Natural Sciences Edition), 2023, 45(1): 2937.
[14] MILO R, SHEN-ORR S, ITZKOVITZ S, et al. Network motifs: simple building blocks of complex networks[J]. Science, 2002, 298(5594): 824827.
[15] PARK Y, LEE M J, SON S W. Motif dynamics in signed directional complex networks[J]. Journal of Korean Physical Society, 2021, 78(6): 535541.
[16] ZHANG Q M, LÜ L, WANG W Q, et al. Potential theory for directed networks[J]. PLOS ONE, 2013, 8(2): e55437.
[17] BENSON A R, ABEBE R, SCHAUB M T, et al. Simplicial closure and higher-order link prediction[J]. Proc Natl Acad Sci, 2018,115(48): e11221e11230.
[18] 曹红艳, 许小可, 许爽. 基于多模体特征的科学家合作预测[J]. 电子科技大学学报, 2020, 49(5): 766773.
CAO H Y, XU X K, XU S. Prediction of scientist collaboration based on multi-modal features[J]. Journal of University of Electronic Science and Technology of China, 2020, 49(5): 766773.
[19] 方祺娜, 许小可. 基于异质模体特征的社交网络链路预测[J]. 电子科技大学学报, 2022, 51(2): 274281.
FANG Q N, XU X K. Link prediction in social networks based on heterogeneous modular features[J]. Journal of University of Electronic Science and Technology of China, 2022, 51(2): 274281.
[20] TRAUD A L, MUCHA P J, PORTER M A. Social structure of facebook networks[J]. Social Science Electronic Publishing, 2012, 391(16): 41654180.
[21] 吕琳媛. 复杂网络链路预测[J]. 电子科技大学学报, 2010, 39(5): 651661.
LÜ L Y. Link prediction in complex networks[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(5): 651661.
[1] 王筱莉, 张静, 陈淑琴, 钱梦迪. 基于社交网络的舆情多信息交互传播机制研究[J]. 复杂系统与复杂性科学, 2024, 21(3): 38-45.
[2] 左子健, 张琳, 吴晔, 许小可. 基于社交网络信息的新冠疫情抽检策略研究[J]. 复杂系统与复杂性科学, 2023, 20(3): 20-26.
[3] 赵薇, 李建波, 吕志强, 董传浩. 融合时间和地理信息的兴趣点推荐研究[J]. 复杂系统与复杂性科学, 2022, 19(4): 25-31.
[4] 王浩, 许小可. 融合文本和表情符号特征的社交网络用户性别识别[J]. 复杂系统与复杂性科学, 2022, 19(4): 17-24.
[5] 王玉, 许楠楠, 胡海波. 社交媒体中的跨平台信息扩散特征及机制[J]. 复杂系统与复杂性科学, 2022, 19(4): 7-16.
[6] 赵炎, 宾晟, 孙更新. 区块链社交网络中信息传播模型研究[J]. 复杂系统与复杂性科学, 2022, 19(2): 1-8.
[7] 闫晓雪, 纪志坚. 社交网络中多领导者观点的博弈建模分析[J]. 复杂系统与复杂性科学, 2022, 19(1): 20-26.
[8] 翁克瑞, 沈卉, 侯俊东. 确定性社会影响力竞争扩散问题研究[J]. 复杂系统与复杂性科学, 2021, 18(4): 21-29.
[9] 公翠娟, 宾晟, 孙更新. 基于多种社交关系的概率矩阵分解推荐算法[J]. 复杂系统与复杂性科学, 2021, 18(1): 1-7.
[10] 周双, 宾晟, 孙更新. 融合多关系的矩阵分解社会化推荐算法[J]. 复杂系统与复杂性科学, 2020, 17(1): 30-36.
[11] 周双, 宾晟, 邵峰晶, 孙更新. 基于多子网复合复杂网络模型的物质扩散推荐算法[J]. 复杂系统与复杂性科学, 2018, 15(4): 77-84.
[12] 吴晓, 刘万平, 杨武, 卢玲, 刘小洋, 黄诗雯. 新型社交网络谣言传播演化模型研究[J]. 复杂系统与复杂性科学, 2018, 15(2): 34-44.
[13] 李沧海, 许益贴, 罗春海, 胡海波. 微博信息扩散的空间分析[J]. 复杂系统与复杂性科学, 2017, 14(3): 75-84.
[14] 朱张祥, 刘咏梅. 在线社交网络谣言传播的仿真研究——基于聚类系数可变的无标度网络环境[J]. 复杂系统与复杂性科学, 2016, 13(2): 74-82.
[15] 杨凯, 刘晓露, 林坚洪, 成曦, 郭强, 刘建国. “冰桶挑战”诱导的社交网络演化分析[J]. 复杂系统与复杂性科学, 2016, 13(2): 90-96.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed