Many real-world networks are characterized by being temporal and dynamic, wherein the temporal information signifies the changes in connections, such as the addition or removal of links between nodes. Employing random walks on these temporal networks is a crucial technique for understanding the structural evolution of such graphs over time. However, existing state-of-the-art sampling methods are designed for traditional static graphs, and as such, they struggle to efficiently handle the dynamic aspects of temporal networks. This deficiency can be attributed to several challenges, including increased sampling complexity, extensive index space, limited programmability, and a lack of scalability.

In this paper, we introduce TEA+, a robust, fast, and scalable engine for conducting random walks on temporal graphs. Central to TEA+ is an innovative hybrid sampling method that amalgamates two Monte Carlo sampling techniques. This fusion significantly diminishes space complexity while maintaining a fast sampling speed. Additionally, TEA+ integrates a range of optimizations that significantly enhance sampling efficiency. This is further supported by an effective graph updating strategy, skilled in managing dynamic graph modifications and adeptly handling the insertion and deletion of both edges and vertices. For ease of implementation, we propose a temporal-centric programming model, designed to simplify the development of various random walk algorithms on temporal graphs. To ensure optimal performance across storage constraints, TEA+ features a degree-aware hybrid storage architecture, capable of adeptly scaling in different memory environments. Experimental results showcase the prowess of TEA+, as it attains up to three orders of magnitude speedups compared to current random walk engines on extensive temporal graphs.

许多现实世界的网络具有时间性和动态性的特点，其中时间信息表示连接的变化，例如节点之间的链接的添加或删除。在这些时间网络上采用随机游走是理解此类图随时间的结构演变的关键技术。然而，现有最先进的采样方法是为传统静态图设计的，因此，它们很难有效地处理时间网络的动态方面。这种缺陷可归因于几个挑战，包括采样复杂性增加、索引空间广泛、可编程性有限以及缺乏可扩展性。

在本文中，我们介绍了TEA+，这是一个强大、快速且可扩展的引擎，用于在时间图上进行随机游走。TEA+的核心是一种创新的混合抽样方法，该方法融合了两种蒙特卡罗抽样技术。这种融合显着降低了空间复杂度，同时保持了快速采样速度。此外，TEA+集成了一系列优化功能，可显着提高采样效率。有效的图更新策略进一步支持这一点，擅长管理动态图修改并熟练处理边和顶点的插入和删除。为了便于实现，我们提出了一种以时间为中心的编程模型，旨在简化时间图上各种随机游走算法的开发。为了确保跨存储限制的最佳性能，TEA+采用了程度感知的混合存储架构，能够在不同的内存环境中熟练地进行扩展。实验结果展示了TEA+的强大功能，与当前的随机游走引擎相比，它在广泛的时间图上实现了高达三个数量级的加速。