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摘要翻译：
提出了一种解决社交网络节点发现问题的方法。隐节点是指不可直接观察到的节点。它们在社会网络中传播影响并影响由此产生的协作活动，但不出现在记录协作活动参与者的监视日志中。发现隐蔽节点就是在隐蔽节点公开的情况下，识别出可能出现隐蔽节点的可疑日志。通过一个由计算合成网络生成的测试数据集和一个真实的组织，证明了该方法的性能。
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英文标题：
《Node discovery problem for a social network》
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作者：
Yoshiharu Maeno
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最新提交年份：
2009
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Methods to solve a node discovery problem for a social network are presented. Covert nodes refer to the nodes which are not observable directly. They transmit the influence and affect the resulting collaborative activities among the persons in a social network, but do not appear in the surveillance logs which record the participants of the collaborative activities. Discovering the covert nodes is identifying the suspicious logs where the covert nodes would appear if the covert nodes became overt. The performance of the methods is demonstrated with a test dataset generated from computationally synthesized networks and a real organization.
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PDF链接：
https://arxiv.org/pdf/0710.4975
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