Detection of influential nodes with multi-scale information*

Abstract

The identification of influential nodes in complex networks is one of the most exciting topics in network science. The latest work successfully compares each node using local connectivity and weak tie theory from a new perspective. We study the structural properties of networks in depth and extend this successful node evaluation from single-scale to multi-scale. In particular, one novel position parameter based on node transmission efficiency is proposed, which mainly depends on the shortest distances from target nodes to high-degree nodes. In this regard, the novel multi-scale information importance (MSII) method is proposed to better identify the crucial nodes by combining the network’s local connectivity and global position information. In simulation comparisons, five state-of-the-art algorithms, i.e. the neighbor nodes degree algorithm (NND), betweenness centrality, closeness centrality, Katz centrality and the k-shell decomposition method, are selected to compare with our MSII. The results demonstrate that our method obtains superior performance in terms of robustness and spreading propagation for both real-world and artificial networks.

Assessing edge-coupled interdependent network disintegration via rank aggregation and elite enumeration

“…For instance, Xu et al [18] proposed an information entropy-based algorithm that considers two-hop information to identify key nodes. Wang et al [19] developed a multi-scale information importance method that integrates local and global information to identify critical nodes more effectively. However, few studies have focused on identifying significant edges in networks, but it is also critical to network disintegration and protection.…”

Self-similarity of complex networks under centrality-based node removal strategy

“…A complex network is composed of a large number of nodes and edges, in which interactions between nodes are very complicated, [1][2][3][4][5] which makes it difficult for us to directly explore and analyze the key attributes from the large network. Therefore, how to "reduce" a large-scale network to a smaller one while preserving the global structure of the network is a common concern of network science researchers.…”