Measure Of Node Importance Research Articles

Node Importance Measurement Based on the Degree and Closeness Centrality

In order to evaluate network node importance efiectively, an approach based on the node importance evaluation matrix is proposed. Difierent from previous node importance measurement approaches, the factors of the node degree and node closeness centrality comprehensively are considered. The time complexity of the algorithm is o(n 3 ). Finally, experimental results show the proposed approach is feasible and efiective.

Node importance measurement based on the degree and clustering coefficient information

The node importance measurement plays an important role in analyzing the robustness of the network. Most researchers use the degree or clustering coefficient to measure the node importance. However, the degree can only take into account the neighbor size, regardless of the clustering property of the neighbors. The clustering coefficient could only measure the closeness among the neighbors and neglect the activity of the target node. In this paper, we present a new method to measure the node importance by combining neighbor and clustering coefficient information. The robustness results measured by the network efficiency through removing the important nodes for the US Air network, the power grid of the western United States and Barabasi-Albert networks show that the new method can more accurately evaluate the node importance than the degree, neighbor information and k-shell indices.

Node importance for dynamical process on networks: A multiscale characterization

Defining the importance of nodes in a complex network has been a fundamental problem in analyzing the structural organization of a network, as well as the dynamical processes on it. Traditionally, the measures of node importance usually depend either on the local neighborhood or global properties of a network. Many real-world networks, however, demonstrate finely detailed structure at various organization levels, such as hierarchy and modularity. In this paper, we propose a multiscale node-importance measure that can characterize the importance of the nodes at varying topological scale. This is achieved by introducing a kernel function whose bandwidth dictates the ranges of interaction, and meanwhile, by taking into account the interactions from all the paths a node is involved. We demonstrate that the scale here is closely related to the physical parameters of the dynamical processes on networks, and that our node-importance measure can characterize more precisely the node influence under different physical parameters of the dynamical process. We use epidemic spreading on networks as an example to show that our multiscale node-importance measure is more effective than other measures.