Graph streams are rapidly evolving sequences of edges that convey continuously changing relationships among entities, playing a crucial role in domains such as networking, finance, and cybersecurity. Their massive scale and high dynamism make obtaining accurate statistics challenging with limited memory constraints. Traditional methods summarize graph streams through hand-crafted sketches, while recent studies have begun to replace these sketches with neural counterparts to improve adaptability and accuracy. However, this shift faces a major challenge: under limited memory, dominant frequent items tend to overshadow rare ones, hindering the neural network's ability to recover accurate statistics. To address this, we propose Crane, a hierarchical neural sketch architecture for graph stream summarization. Crane uses a hierarchical carry mechanism that automatically elevates frequent items to higher memory layers, reducing interference between frequent and infrequent items within the same layer. To better accommodate real-world deployment, Crane further adopts an adaptive memory expansion strategy that dynamically adds new layers once the occupancy of the top layer exceeds a threshold, enabling scalability across diverse data magnitudes. Extensive experiments on various datasets ranging from 20K to 60M edges demonstrate that Crane reduces estimation error by roughly 10x compared to state-of-the-art methods.

翻译：图流是快速演化的边序列，持续传递实体间不断变化的关系，在网络、金融和网络安全等领域扮演关键角色。其海量规模与高度动态性使得在有限内存约束下获取精确统计量极具挑战。传统方法通过手工设计的草图对图流进行摘要，而近期研究开始用神经草图替代这些设计以提升适应性与准确性。然而，这一转变面临一个主要挑战：在有限内存下，占主导的频繁项往往会掩盖罕见项，阻碍神经网络恢复准确统计量的能力。为解决此问题，我们提出Crane——一种用于图流摘要的分层神经草图架构。Crane采用分层进位机制，自动将频繁项提升至更高内存层级，减少同层内频繁项与罕见项间的相互干扰。为更好地适应实际部署需求，Crane进一步采用自适应内存扩展策略：一旦顶层占用率超过阈值即动态添加新层级，从而实现对不同数据规模的扩展性。在20K至60M边的多种数据集上的大量实验表明，相较于最先进方法，Crane将估计误差降低了约10倍。