[计算机科学] 网络组织中的节点发现 [推广有奖]

摘要翻译：
本文提出了一种解决网络组织中节点发现问题的方法。隐节点是指不可直接观察到的节点。它们影响社会互动，但不出现在记录社会互动参与者的监视日志中。隐节点发现是指当隐节点公开时，识别出可能出现隐节点的可疑日志。建立了社交网络最大似然估计和可疑日志识别的数学模型。用真实组织生成的数据集和计算合成的数据集证明了精度、查全率和F测度特征。对于任何拓扑结构和规模的网络中的任何隐蔽节点，如果观测数目与可能的通信模式数目之比较大，则其性能接近理论极限。
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英文标题：
《Node discovery in a networked organization》
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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：
  In this paper, I present a method to solve a node discovery problem in a networked organization. Covert nodes refer to the nodes which are not observable directly. They affect social interactions, but do not appear in the surveillance logs which record the participants of the social interactions. Discovering the covert nodes is defined as identifying the suspicious logs where the covert nodes would appear if the covert nodes became overt. A mathematical model is developed for the maximal likelihood estimation of the network behind the social interactions and for the identification of the suspicious logs. Precision, recall, and F measure characteristics are demonstrated with the dataset generated from a real organization and the computationally synthesized datasets. The performance is close to the theoretical limit for any covert nodes in the networks of any topologies and sizes if the ratio of the number of observation to the number of possible communication patterns is large.
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PDF链接：
https://arxiv.org/pdf/0803.3363