论文标题
超图中的核心外围检测
Core-periphery detection in hypergraphs
论文作者
论文摘要
核心外围检测是探索性网络分析中的关键任务，其中一个人旨在在内部和外围找到一组核心，一组良好的节点连接，以及一个外围，一组仅连接（或大部分）与核心连接的节点。在这项工作中，我们为建模为HyperGraphs的高阶网络提出了一个核心外围的模型，我们提出了一种计算核心评分向量的方法，该方法可以量化每个节点与核心的距离。特别是，我们表明该方法在全球范围内解决了相应的非凸线核心 - 外围优化问题，以提高任意精度。事实证明，这种方法与非线性超图操作员的perron特征向量的计算相吻合，该方法适当地定义了超图的入射矩阵，概括了最近提出的超透明的中心性模型。我们对合成和现实世界中的超图进行了几项实验，表明所提出的方法优于替代性核心周期检测算法，特别是通过将已建立的图形方法传递到通过集团扩展为超透明设置而获得的方法。
Core-periphery detection is a key task in exploratory network analysis where one aims to find a core, a set of nodes well-connected internally and with the periphery, and a periphery, a set of nodes connected only (or mostly) with the core. In this work we propose a model of core-periphery for higher-order networks modeled as hypergraphs and we propose a method for computing a core-score vector that quantifies how close each node is to the core. In particular, we show that this method solves the corresponding non-convex core-periphery optimization problem globally to an arbitrary precision. This method turns out to coincide with the computation of the Perron eigenvector of a nonlinear hypergraph operator, suitably defined in term of the incidence matrix of the hypergraph, generalizing recently proposed centrality models for hypergraphs. We perform several experiments on synthetic and real-world hypergraphs showing that the proposed method outperforms alternative core-periphery detection algorithms, in particular those obtained by transferring established graph methods to the hypergraph setting via clique expansion.
