标题： SASH：图中的社区结构解码 显示英文标题

标题： SASH: Decoding Community Structure in Graphs

摘要： 检测图中的社区涉及识别紧密连接顶点的聚类; 该领域有各种重要的应用和丰富的文献。 以前，通过将图视为假设底层社区的噪声版本，这个问题被置于纠错码的领域中。 在本文中，我们引入了社区结构的编码以及由此产生的代码参数。 然后，我们提出了一种新的算法 SASH，在给定观察数据集的情况下解码以估计社区。 我们通过在同配性植株划分模型和 Zachary 的空手道俱乐部数据集上的模拟来展示 SASH 的性能。 显示英文摘要

摘要： Detection of communities in a graph entails identifying clusters of densely connected vertices; the area has a variety of important applications and a rich literature. The problem has previously been situated in the realm of error correcting codes by viewing a graph as a noisy version of the assumed underlying communities. In this paper, we introduce an encoding of community structure along with the resulting code's parameters. We then present a novel algorithm, SASH, to decode to estimated communities given an observed dataset. We demonstrate the performance of SASH via simulations on an assortative planted partition model and on the Zachary's Karate Club dataset.