Identifying critical nodes in complex networks based on neighborhood information

Abstract

The identification of important nodes in complex networks has always been a prominent topic in the field of network science. Nowadays, the emergence of large-scale networks has sparked our research interest in complex network centrality methods that balance accuracy and efficiency. Therefore, this paper proposes a novel centrality method called Spon (Sum of the Proportion Of Neighbors) Centrality, which combines algorithmic efficiency and accuracy. Spon only requires information within the three-hop neighborhood of a node to assess its centrality, thereby exhibiting lower time complexity and suitability for large-scale networks. To evaluate the performance of Spon, we conducted connectivity tests on 16 empirical unweighted networks and compared the monotonicity and algorithmic efficiency of Spon with other methods. Experimental results demonstrate that Spon achieves both accuracy and algorithmic efficiency, outperforming eight other methods, including CycleRatio, Collective Influence, and Social Capital. Additionally, we present a method called W-Spon to extend Spon to weighted networks. Comparative experimental results on 10 empirical weighted networks illustrate that W-Spon also possesses advantages compared to methods such as I-Core and M-Core.

A Novel Method to Identify Key Nodes in Complex Networks Based on Degree and Neighborhood Information

“…(9) Yeast: Describing the interactions of proteins within yeast cells [43]. (10) Hamsterster: A website network that connects user friendships and family relationships [38]. (11) Kohonen: A network based on the neural network model of self-organizing maps, initially proposed for the citation network analysis in Pajek [44].…”

“…The basic topological features of the network number are shown in Table 3. Connectivity testing stands as one of the classical validation methods for assessing the importance of nodes within a network [10]. The importance sequence of nodes is established through a designated algorithm, after which these nodes are systematically removed one by one.…”

“…Over the past few decades, many efficient methods have been developed to assess the nodes' importance by leveraging both local and global information, along with other relevant attributes. For example, EnRenew [9] utilizes local entropy to pinpoint key nodes within networks; Spon [10] assesses node importance using information from neighboring nodes; VoteRank [11] employs a voting mechanism to recognize significant disseminators in networks; and FINDER [12] utilizes reinforcement learning to identify crucial nodes within networks.…”

“…Current investigations into extracting critical information from complex networks have frequently presented researchers with the challenge of reconciling method efficiency with precision [10]. Therefore, based on degree and neighbor node information, this paper introduces an innovative approach to identify crucial nodes within networks, referred to as the degree and neighborhood information centrality (DNC).…”

The Identification of Influential Nodes Based on Neighborhood Information in Asymmetric Networks

“…This study primarily focuses on methodologies for quantifying critical nodes, emphasizing network structural attributes, particularly leveraging local structural characteristics. Notably, a novel centrality method, SPON (sum of proportion of neighbors) [25], balances algorithmic efficiency and accuracy. Despite its lower time complexity for large-scale networks, SPON falls short of accuracy compared to the degree and h-index.…”