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.
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.
[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.
