| 引用本文: | 赛影辉,王明鑫,陈畅,雷伯涵,侯叶俏,李翔翔,孙月明,陈旭.基于感染结果的传播网络推断方法.软件学报,2022,33(8):3103-3114 |
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| 基于感染结果的传播网络推断方法 |
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赛影辉1, 王明鑫1, 陈畅1, 雷伯涵2, 侯叶俏1, 李翔翔3, 孙月明1, 陈旭1
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1.武汉大学 计算机学院, 湖北 武汉 430072;2.浙江大学 计算机科学与技术学院, 浙江 杭州 310027;3.航天恒星科技有限公司, 北京 100086
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| 摘要: |
| 为揭示传播网络中节点之间的父子影响关系,现有工作大多需要知道节点的感染时间,而该信息往往只有通过对传播过程进行实时监控才能获得.研究如何基于传播结果来学习获得传播网络中节点之间的父子影响关系.传播结果只包含每个传播过程中节点的最终感染状态,而节点的最终感染状态在实际中往往比节点的感染时间更容易获得.提出了一种基于条件熵的方法来推断网络中每个节点的潜在候选父节点.此外,能够通过从基于条件熵的推断结果中发现并修剪那些实际不太可能存在的父子影响关系来优化最终的影响关系推断结果.在人工网络和真实网络上的大量实验,验证了该方法的有效性和运行效率. |
| 关键词: 传播网络推断 影响关系 感染结果 |
| DOI：10.13328/j.cnki.jos.006283 |
| 分类号:TP309 |
| 基金项目:民用航天“十三五”技术预先研究项目(B0301);湖北省技术创新专项重大项目(2017AAA125);武汉市应用基础前沿项目(2018010401011288) |
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| Diffusion Network Inference Based on Infection Results |
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SAI Ying-Hui1, WANG Ming-Xin1, CHEN Chang1, LEI Bo-Han2, HOU Ye-Qiao1, LI Xiang-Xiang3, SUN Yue-Ming1, CHEN Xu1
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1.School of Computer Science, Wuhan University, Wuhan 430072, China;2.College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China;3.Space Star Technology Co. Ltd., Beijing 100086, China
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| Abstract: |
| To reveal parent-child influence relationships between nodes in a diffusion network, most prior work requires knowledge of node infection time, which is possible only by carefully monitoring the diffusion process. This work investigates how to solve this problem by learning from diffusion results, which contain only the final infection statuses of nodes in each diffusion process and are often more easily accessible in practice. A conditional entropy-based method is presented to infer potential candidate parent nodes for each node in the network. Furthermore, the inference results are able to be refined by identifying and pruning the inferred influence relations that are unlikely to exist in reality. Experimental results on both synthetic and real-world networks verify the effectiveness and efficiency of our approach. |
| Key words: diffusion network inference influence relationships infection results |
