Temporal networks representing a stream of timestamped edges are seemingly ubiquitous in the real world. However, the massive size and continuous nature of these networks make them fundamentally challenging to analyze and leverage for descriptive and predictive modeling tasks. In this work, we propose a general framework for temporal network sampling with unbiased estimation. We develop online, single-pass sampling algorithms, and unbiased estimators for temporal network sampling. The proposed algorithms enable fast, accurate, and memory-efficient statistical estimation of temporal network patterns and properties. In addition, we propose a temporally decaying sampling algorithm with unbiased estimators for studying networks that evolve in continuous time, where the strength of links is a function of time, and the motif patterns are temporally weighted. In contrast to the prior notion of a △ t -temporal motif, the proposed formulation and algorithms for counting temporally weighted motifs are useful for forecasting tasks in networks such as predicting future links, or a future time-series variable of nodes and links. Finally, extensive experiments on a variety of temporal networks from different domains demonstrate the effectiveness of the proposed algorithms. A detailed ablation study is provided to understand the impact of the various components of the proposed framework.

中文翻译：

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时态网络的在线采样

表示带时间戳的边缘流的时间网络在现实世界中似乎无处不在。然而，这些网络的巨大规模和连续性使得它们在分析和利用描述性和预测性建模任务方面具有根本性的挑战。在这项工作中，我们提出了一个具有无偏估计的时间网络采样的通用框架。我们开发了用于时间网络采样的在线单通道采样算法和无偏估计器。所提出的算法能够对时间网络模式和属性进行快速、准确和内存高效的统计估计。此外，我们提出了一种具有无偏估计量的时间衰减采样算法，用于研究在连续时间中演化的网络，其中链接的强度是时间的函数，并且主题图案是时间加权的。与之前的 △ 概念相反吨-时间主题，用于计算时间加权主题的建议公式和算法可用于预测网络中的任务，例如预测未来链接或节点和链接的未来时间序列变量。最后，对来自不同领域的各种时间网络的广泛实验证明了所提出算法的有效性。提供了详细的消融研究，以了解拟议框架的各个组成部分的影响。