Studies of networked phenomena, such as interactions in online social media, often rely on incomplete data, either because these phenomena are partially observed, or because the data is too large or expensive to acquire all at once. Analysis of incomplete data leads to skewed or misleading results. In this paper, we investigate limitations of learning to complete partially observed networks via node querying. Concretely, we study the following problem: given (i) a partially observed network, (ii) the ability to query nodes for their connections (e.g., by accessing an API), and (iii) a budget on the number of such queries, sequentially learn which nodes to query in order to maximally increase observability. We call this querying process Network Online Learning and present a family of algorithms called NOL*. These algorithms learn to choose which partially observed node to query next based on a parameterized model that is trained online through a process of exploration and exploitation. Extensive experiments on both synthetic and real world networks show that (i) it is possible to sequentially learn to choose which nodes are best to query in a network and (ii) some macroscopic properties of networks, such as the degree distribution and modular structure, impact the potential for learning and the optimal amount of random exploration.

中文翻译：

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了解网络在线学习的局限性

对网络现象的研究（例如，在线社交媒体中的互动）通常依赖于不完整的数据，这可能是因为部分观察到了这些现象，或者是因为数据太大或太昂贵而无法一次获取所有信息。分析不完整的数据会导致结果偏斜或误导。在本文中，我们研究了通过节点查询来学习以完成部分观察到的网络的局限性。具体来说，我们研究以下问题：给定（i）部分观察到的网络，（ii）能够查询节点的连接（例如，通过访问API）的能力，以及（iii）这样的查询数量的预算，顺序了解要查询的节点，以最大程度地提高可观察性。我们称这个查询过程ñ etwork Ø n第大号获得并提出了一系列称为NOL *的算法。这些算法学习如何基于参数化模型选择要查询的部分观测节点，该参数化模型是通过探索和开发过程进行在线训练的。在综合和现实世界网络上进行的大量实验表明，（i）可以顺序学习选择哪个节点在网络中最适合查询，并且（ii）网络的某些宏观属性，例如度分布和模块化结构，影响学习潜力和随机探索的最佳数量。