| 耦合网络视角下微信群间的知识传播模型 |
| Knowledge dissemination model between WeChat groups from the perspective of coupled networks |
| 摘要点击 75 全文点击 0 投稿时间：2024-09-13 修订日期：2025-09-28 |
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| 中文关键词 知识传播; 微信群之间; 耦合网络; 复杂网络传播动力学; 阈值 |
| 英文关键词 knowledge dissemination; between WeChat groups; coupled networks; transmission dynamics in complex networks; threshold |
| 基金项目 国家自然科学基金项目（面上项目，重点项目，重大项目） |
| 投稿方向 复杂系统管理 |
| 作者 | 单位 | 邮编 | | 朱宏淼 | 上海对外经贸大学 | 201620 | | 齐佳音* | 广州大学
北京邮电大学 | 510006 | | 靳祯 | 山西大学 | | | 张生太 | 北京邮电大学 | | | 周慧敏 | 上海交通大学 | |
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| 中文摘要 |
| 聚焦跨微信群知识传播效能低下瓶颈, 首先基于耦合网络理论, 创新性地将含有共同用户的两微信群知识传播系统抽象为部分节点重叠的双层网络, 突破传统单层及多层网络模型的局限, 建立耦合网络视角下微信群间知识传播动力学模型. 其次, 通过稳定性分析推导出知识跨群传播的阈值, 为量化传播效率提供理论依据. 阈值较小时知识在双群流失; 较大时知识可能跨群传播. 最后, 对跨微信群知识传播进行参数估计、案例及仿真分析. 研究发现: 若跨群用户在单一群内过度活跃, 知识虽于该微信群迅速扩散, 却显著抑制其在另一群传播; 此类用户需在各群适度交流, 并均衡运用@提及与随机交流, 方能实现知识在双群传播. 跨群用户超过临界规模, 可驱动知识在双层网络扩散, 无需各群均满足传播的阈值条件. |
| 英文摘要 |
| Focusing on the bottleneck of low efficiency for knowledge dissemination between WeChat groups, knowledge propagation system in two groups with shared users is abstracted as two-layer networks with overlapping nodes based on coupled networks theory. It overcomes limitations of traditional single-layer and multi-layer networks models. A dynamics model of knowledge dissemination between WeChat groups is established from perspective of coupled networks. Stability analysis derives threshold for knowledge propagation across groups, providing theoretical basis for quantifying dissemination efficiency. A lower threshold causes knowledge loss in two groups and a higher one enables cross-group propagation. Parameter estimation, case and simulation analyses are conducted on dissemination cross groups. It finds that if cross-group users are overly active in one group, knowledge diffuses rapidly in it but significantly suppresses propagation in the other. Effective knowledge propagation requires these users to maintain moderate activity in each group and use @mentions and random communication in balanced way. Once the number of cross-group users exceeds critical mass, knowledge can propagate in two-layer networks without each group individually meeting threshold. |
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