幂律图的近优邻接标号方案
Near-optimal adjacency labeling scheme for power-law graphs
摘要：
邻接标记方案是一种将标签分配给图的顶点的方法，使得可以直接从所分配的标签推断顶点之间的邻接，而不使用集中式数据结构。我们设计的邻接标签计划的家庭的幂律图。这个族已被用于对许多类型的网络进行建模，例如Internet AS级图。此外，我们证明了几乎匹配的下界这个家庭。我们还提供了一个渐近近最优的标记计划稀疏图。最后，我们通过使用合成数据和高达数十万个顶点的真实网络的实验评估来验证我们的标记方案的效率。
英文摘要：
An adjacency labeling scheme is a method that assigns labels to the vertices of a graph such that adjacency between vertices can be inferred directly from the assigned label, without using a centralized data structure. We devise adjacency labeling schemes for the family of power-law graphs. This family that has been used to model many types of networks, e.g. the Internet AS-level graph. Furthermore, we prove an almost matching lower bound for this family. We also provide an asymptotically near- optimal labeling scheme for sparse graphs. Finally, we validate the efficiency of our labeling scheme by an experimental evaluation using both synthetic data and real-world networks of up to hundreds of thousands of vertices.
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。
