[1] HOW E J. The rise of crowdsourcing[J]. Wired Magazine, 2006, 14 (6): 176-183. [2] 夏恩君, 赵轩维, 李森. 国外众包研究现状和趋势[J]. 技术经济, 2015, 34(1): 28-36 XIA Enjun, ZHAO Xuanwei, LI Sen. Crowdsourcing research: statue and prospect[J]. Technical Economy, 2015, 34(1): 28-36 [3] 王震, 欧阳啸, 郭伟. 众包设计平台工作者关系网络构建与分析[J]. 计算机集成制造系统, 2022, 28(8): 2522-2533. WANG Zhen, OUYANG Xiao, GUO Wei. Construction and analysis of worker’s relationship network of crowdsourcing design platform[J]. Computer Integrated Manufacturing Systems, 2022, 28(8): 2522-2533. [4] 潘仁志, 钱付兰, 赵姝, 等. 基于卷积神经网络交互的用户属性偏好建模的推荐模型[J]. 计算机应用, 2022, 42(2): 404-411 PAN Renzhi, QIAN Fulan, ZHAO Shu, et al. Recommendation model for user attribute preference modeling based on convolutional neural network interaction[J]. Journal of Computer Applications, 2022, 42(2): 404-411 [5] 王晓燕, 李劲华. 用户偏好模型在众包中应用的研究[J]. 青岛大学学报 (自然科学版), 2018, 31(1): 102-108 WANG Xiaoyan, LI Jinhua. The Research of the Applying User Interest Model to Crowdsourcing[J]. Journal of Qingdao University (Natural Science Edition), 2018, 31(1): 102-108 [6] 武聪, 马文明, 王冰, 等. 融合用户标签相似度的矩阵分解算法[J]. 南京大学学报 (自然科学), 2022, 58(1): 143-152 WU Cong, MA Wenming, WANG Bing, et al. Matrix factorization algorithm combined with user tag similarity[J]. Journal of Nanjing University (Natural Science), 2022, 58(1): 143-152 [7] HORVATH T. A model of user preference learning for content-based recommender systems[J]. Computing & Informatics, 2012, 28(4): 453-481 [8] LIN C. Signals in the silence: models of implicit feedback in a recommendation system for crowdsourcing[C]//Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. Quebec City, Canada: AAAI Press, 2014: 908-915. [9] GUNNEMANN S, FARBER I, RAUBACH S, et al. Spectral subspace clustering for graphs with feature vectors[C]//Proceedings of 14th International Conference on Data Mining. NY: IEEE Computer Society, 2014, 231-240. [10] 姜东明, 杨火根. 融合图卷积网络模型的无监督社区检测算法[J]. 计算机工程与应用, 2020, 56(20): 59-66 JIANG Dongming, YANG Huogen. Unsupervised community detection algorithm integrating graph convolutional network model[J]. Computer Engineering and Applications, 2020, 56(20): 59-66 [11] WU P, PAN L. Mining target attribute subspace and set of target communities in large attributed networks[EB/OL]. (2017-05-10). http://arXiv.org/abs/1705.03590vl, 2017. [12] 孟彩霞, 李楠楠, 张琰. 基于复杂网络的社区发现算法研究[J]. 计算机技术与发展, 2020, 30(1): 82-86 MENG Caixia, LI Nannan, ZHANG Yan. Research on community detection algorithm based on complex network[J]. Computer Technology and Development, 2020, 30(1): 82-86 [13] 王静红, 于雅智. 复杂网络半监督的社区发现算法研究[J]. 计算机应用研究, 2018, 35(6): 1663-1667 WANG Jinghong, YU Yazhi. Research algorithm of semi-supervised general community detection on complex networks[J]. Application Research of Computers, 2018, 35(6): 1663-1667 [14] 杨晓光, 朱保平. 基于复杂网络的社区发现算法[J]. 南京理工大学学报, 2016, 40(3): 267-271 YANG Xiaoguang, ZHU Baoping. Community detection algorithm based on complex network[J]. Journal of Nanjing University of Science and Technology, 2016, 40(3): 267-271 [15] 王亚珅, 黄河燕, 冯冲. 基于最小割图分割的社区发现算法[J]. 中文信息学报, 2017, 31(3): 213-222 WANG Yashen, HUANG Heyan, FENG Chong. Community detection based on minimum-cut graph partitioning[J]. Journal of Chinese Information Processing, 2017, 31(3): 213-222 [16] 褚叶祺, 丁佳骏. 基于Louvain算法的作者合著网络社区划分研究[J]. 高技术通讯, 2021, 31(3): 257-262 CHU Yeqi, DING Jiajun. Research on community detetion in co-authorship networks based on Louvain algorithm[J]. Chinese High Technology Letters, 2021, 31(3): 257-262 [17] 吴祖峰, 王鹏飞, 秦志光, 等. 改进的Louvain社团划分算法[J]. 电子科技大学学报, 2013, 42(1): 105-108 WU Zufeng, WANG Pengfei, QIN Zhiguang, et al. Improved algorithm of Louvain communities dipartition[J]. Journal of University of Electronic Science and Technology of China, 2013, 42(1): 105-108 [18] NEWMAN M E J. Spectral methods for community detection and graph partitioning[J]. Physical Review E-Statistical, Nonlinear and Soft Matter Physics, 2013, 88(4-1): 042822 [19] 夏玮, 杨鹤标. 改进的Louvain算法及其在推荐领域的研究[J]. 信息技术, 2017(11): 125-128 XIA Wei, YANG Hebiao. Optimization of Louvain algorithm and its application in personalized recommendation[J]. Information Technology, 2017(11): 125-128 [20] 王海燕, 孙成成. 一种基于多视图学习的群组发现方法[J]. 南京邮电大学学报 (自然科学版), 2019, 39(4): 80-87 WANG Haiyan, SUN Chengcheng. Group discovery method based on multi-view learning[J]. Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition), 2019, 39(4): 80-87
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