快速准确的图流摘要
Fast and Accurate Graph Stream Summarization
摘要：
图流是连续的数据项序列，其中每个项指示一条边，包括其两个端点和边权重。它形成一个动态图，随流中的每个项目而变化。图流在网络安全、社交网络、云故障排除系统等领域发挥着重要作用。由于图流的巨大容量和高更新速度，传统的图存储数据结构，如邻接矩阵和邻接表已经不能满足需要。然而，图流摘要的现有技术，如CM sketches、gSketches、TCM和gMatrix，要么支持有限种类的查询，要么遭受查询结果的不良准确性。本文提出了一种新的图流草图（Graph Stream Sketch，简称GSS）来概括图流，它具有线性空间代价（O（|E|），E为图的边集）和常数更新时间复杂度（O（1）），支持对图流的各种查询，且错误可控.理论分析和实验结果都证实了该方案在时间/空间复杂度和查询结果精度方面的优越性。
英文摘要：
A graph stream is a continuous sequence of data items, in which each item indicates an edge, including its two endpoints and edge weight. It forms a dynamic graph that changes with every item in the stream. Graph streams play important roles in cyber security, social networks, cloud troubleshooting systems and other fields. Due to the vast volume and high update speed of graph streams, traditional data structures for graph storage such as the adjacency matrix and the adjacency list are no longer sufficient. However, prior art of graph stream summarization, like CM sketches, gSketches, TCM and gMatrix, either supports limited kinds of queries or suffers from poor accuracy of query results. In this paper, we propose a novel Graph Stream Sketch (GSS for short) to summarize the graph streams, which has the linear space cost (O(|E|), E is the edge set of the graph) and the constant update time complexity (O(1)) and supports all kinds of queries over graph streams with the controllable errors. Both theoretical analysis and experiment results confirm the superiority of our solution with regard to the time/space complexity and query results' precision compared with the state-of-the-art.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
