论文标题
具有依赖节点重要性的多层网络的特征向量中心性
Eigenvector centrality for multilayer networks with dependent node importance
论文作者
论文摘要
我们提出了一种用于计算具有层间限制的多层网络特征向量中心性变体的新方法。具体而言，我们考虑了由多个边缘加权，可能定向的多层网络在同一节点集上，每个图表都代表网络的一层而没有层间边缘。与标准特征向量中心性结构一样，给定层中每个节点的重要性基于同一层中相邻节点重要性的加权总和。与标准特征向量的中心性不同，我们假设邻接关系和相邻节点的重要性可能基于不同的层。重要的是，这种类型的中心性约束仅由多层特征向量中心的现有框架部分支持，这些框架使用不同层中节点之间的边缘来捕获层间依赖性。对于我们的模型，受约束，特定的特征向量中心性值由独立特征值问题和依赖的伪元值问题定义，其解决方案可以使用交织的电源迭代迭代算法有效地实现。
We present a novel approach for computing a variant of eigenvector centrality for multilayer networks with inter-layer constraints on node importance. Specifically, we consider a multilayer network defined by multiple edge-weighted, potentially directed, graphs over the same set of nodes with each graph representing one layer of the network and no inter-layer edges. As in the standard eigenvector centrality construction, the importance of each node in a given layer is based on the weighted sum of the importance of adjacent nodes in that same layer. Unlike standard eigenvector centrality, we assume that the adjacency relationship and the importance of adjacent nodes may be based on distinct layers. Importantly, this type of centrality constraint is only partially supported by existing frameworks for multilayer eigenvector centrality that use edges between nodes in different layers to capture inter-layer dependencies. For our model, constrained, layer-specific eigenvector centrality values are defined by a system of independent eigenvalue problems and dependent pseudo-eigenvalue problems, whose solution can be efficiently realized using an interleaved power iteration algorithm.
