Because of the complex activities involved in IoT networks of a smart city, an important question arises: What are the core activities of the networks as a whole and its basic information flow structure? Identifying and discovering core activities and information flow is a crucial step that can facilitate the analysis. This is the question we are addressing—that is, to identify the core services as a common core substructure despite the probabilistic nature and the diversity of its activities. If this common substructure can be discovered, a systemic analysis and planning can then be performed and key policies related to the community can be developed. Here, a local IoT network can be represented as an attributed graph. From an ensemble of attributed graphs, identifying the common subgraph pattern is then critical in understanding the complexity. We introduce this as the common random subgraph (CRSG) modeling problem, aiming at identifying a subgraph pattern that is the structural “core” that conveys the probabilistically distributed graph characteristics. Given an ensemble of network samples represented as attributed graphs, the method generates a CRSG model that encompasses both structural and statistical characteristics from the related samples while excluding unrelated networks. In generating a CRSG model, our method using a multiple instance learning algorithm transforms an attributed graph (composed of structural elements as edges and their two endpoints) into a “bag” of instances in a vector space. Common structural components across positively labeled graphs are then identified as the common instance patterns among instances across different bags. The structure of the CRSG arises through the combining of common patterns. The probability distribution of the CRSG can then be estimated based on the connections and distributions from the common elements. Experimental results demonstrate that CRSG models are highly expressive in describing typical network characteristics.

中文翻译：

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面向结构化物联网智能城市应用的基于随机图的多实例学习

由于智慧城市物联网网络所涉及的活动复杂，因此出现了一个重要问题：整个网络的核心活动及其基本信息流结构是什么？识别和发现核心活动和信息流是促进分析的关键步骤。这就是我们要解决的问题——即，将核心服务识别为一个共同的核心子结构，尽管其活动具有概率性质和多样性。如果可以发现这个共同的子结构，就可以进行系统分析和规划，并制定与社区相关的关键政策。在这里，本地物联网网络可以表示为属性图。从属性图的集合中，识别公共子图模式对于理解复杂性至关重要。我们将此作为通用随机子图（CRSG）建模问题引入，旨在识别子图模式，该模式是传达概率分布图特征的结构“核心”。给定表示为属性图的网络样本集合，该方法生成一个 CRSG 模型，该模型包含来自相关样本的结构和统计特征，同时排除不相关的网络。在生成 CRSG 模型时，我们使用多实例学习算法的方法将属性图（由作为边及其两个端点的结构元素组成）转换为向量空间中的实例“包”。然后将正标记图上的公共结构组件识别为不同袋子实例之间的公共实例模式。CRSG 的结构是通过组合常见模式而产生的。然后可以根据来自公共元素的连接和分布来估计 CRSG 的概率分布。实验结果表明，CRSG模型在描述典型网络特征方面具有很强的表现力。